article with hypothesis

HOW TO: Use Articles for Research: Introduction: Hypothesis/Thesis

• What's a Scholarly Journal?
• Reading the Citation
• Authors' Credentials
• Introduction: Hypothesis/Thesis
• Literature Review
• Research Method
• Results/Data
• Discussion/Conclusions

Hypothesis or Thesis

The first few paragraphs of a journal article serve to introduce the topic, to provide the author's hypothesis or thesis, and to indicate why the research was done.  A thesis or hypothesis is not always clearly labled; you may need to read through the introductory paragraphs to determine what the authors are proposing.

• << Previous: Abstract
• Next: Literature Review >>
• Last Updated: Jan 29, 2024 3:35 PM
• URL: https://libguides.cayuga-cc.edu/1ST-PRIORITY/articles

• Resources Home 🏠
• Try SciSpace Copilot
• Search research papers
• Add Copilot Extension
• Try AI Detector
• Try Paraphraser
• Try Citation Generator
• April Papers
• June Papers
• July Papers

The Craft of Writing a Strong Hypothesis

Table of Contents

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper .

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good  alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

• Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
• Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher  the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

• A research hypothesis has to be simple yet clear to look justifiable enough.
• It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
• It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
• A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
• If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
• A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

You might also like

Consensus GPT vs. SciSpace GPT: Choose the Best GPT for Research

Literature Review and Theoretical Framework: Understanding the Differences

Types of Essays in Academic Writing - Quick Guide (2024)

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology
• How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more variables . An independent variable is something the researcher changes or controls. A dependent variable is something the researcher observes and measures.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

Prevent plagiarism, run a free check.

Step 1: ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2: Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalise more complex constructs.

Step 3: Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

Step 4: Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

Step 5: Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in if … then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

Step 6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis. The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

A hypothesis is not just a guess. It should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2022, May 06). How to Write a Strong Hypothesis | Guide & Examples. Scribbr. Retrieved 5 August 2024, from https://www.scribbr.co.uk/research-methods/hypothesis-writing/

Is this article helpful?

Shona McCombes

Other students also liked, operationalisation | a guide with examples, pros & cons, what is a conceptual framework | tips & examples, a quick guide to experimental design | 5 steps & examples.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Hypothesis Testing | A Step-by-Step Guide with Easy Examples

Published on November 8, 2019 by Rebecca Bevans . Revised on June 22, 2023.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics . It is most often used by scientists to test specific predictions, called hypotheses, that arise from theories.

There are 5 main steps in hypothesis testing:

• State your research hypothesis as a null hypothesis and alternate hypothesis (H o ) and (H a  or H 1 ).
• Collect data in a way designed to test the hypothesis.
• Perform an appropriate statistical test .
• Decide whether to reject or fail to reject your null hypothesis.
• Present the findings in your results and discussion section.

Though the specific details might vary, the procedure you will use when testing a hypothesis will always follow some version of these steps.

Table of contents

Step 1: state your null and alternate hypothesis, step 2: collect data, step 3: perform a statistical test, step 4: decide whether to reject or fail to reject your null hypothesis, step 5: present your findings, other interesting articles, frequently asked questions about hypothesis testing.

After developing your initial research hypothesis (the prediction that you want to investigate), it is important to restate it as a null (H o ) and alternate (H a ) hypothesis so that you can test it mathematically.

The alternate hypothesis is usually your initial hypothesis that predicts a relationship between variables. The null hypothesis is a prediction of no relationship between the variables you are interested in.

• H 0 : Men are, on average, not taller than women. H a : Men are, on average, taller than women.

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

For a statistical test to be valid , it is important to perform sampling and collect data in a way that is designed to test your hypothesis. If your data are not representative, then you cannot make statistical inferences about the population you are interested in.

There are a variety of statistical tests available, but they are all based on the comparison of within-group variance (how spread out the data is within a category) versus between-group variance (how different the categories are from one another).

If the between-group variance is large enough that there is little or no overlap between groups, then your statistical test will reflect that by showing a low p -value . This means it is unlikely that the differences between these groups came about by chance.

Alternatively, if there is high within-group variance and low between-group variance, then your statistical test will reflect that with a high p -value. This means it is likely that any difference you measure between groups is due to chance.

Your choice of statistical test will be based on the type of variables and the level of measurement of your collected data .

• an estimate of the difference in average height between the two groups.
• a p -value showing how likely you are to see this difference if the null hypothesis of no difference is true.

Based on the outcome of your statistical test, you will have to decide whether to reject or fail to reject your null hypothesis.

In most cases you will use the p -value generated by your statistical test to guide your decision. And in most cases, your predetermined level of significance for rejecting the null hypothesis will be 0.05 – that is, when there is a less than 5% chance that you would see these results if the null hypothesis were true.

In some cases, researchers choose a more conservative level of significance, such as 0.01 (1%). This minimizes the risk of incorrectly rejecting the null hypothesis ( Type I error ).

Prevent plagiarism. Run a free check.

The results of hypothesis testing will be presented in the results and discussion sections of your research paper , dissertation or thesis .

In the results section you should give a brief summary of the data and a summary of the results of your statistical test (for example, the estimated difference between group means and associated p -value). In the discussion , you can discuss whether your initial hypothesis was supported by your results or not.

In the formal language of hypothesis testing, we talk about rejecting or failing to reject the null hypothesis. You will probably be asked to do this in your statistics assignments.

However, when presenting research results in academic papers we rarely talk this way. Instead, we go back to our alternate hypothesis (in this case, the hypothesis that men are on average taller than women) and state whether the result of our test did or did not support the alternate hypothesis.

If your null hypothesis was rejected, this result is interpreted as “supported the alternate hypothesis.”

These are superficial differences; you can see that they mean the same thing.

You might notice that we don’t say that we reject or fail to reject the alternate hypothesis . This is because hypothesis testing is not designed to prove or disprove anything. It is only designed to test whether a pattern we measure could have arisen spuriously, or by chance.

If we reject the null hypothesis based on our research (i.e., we find that it is unlikely that the pattern arose by chance), then we can say our test lends support to our hypothesis . But if the pattern does not pass our decision rule, meaning that it could have arisen by chance, then we say the test is inconsistent with our hypothesis .

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Descriptive statistics
• Measures of central tendency
• Correlation coefficient

Methodology

• Cluster sampling
• Stratified sampling
• Types of interviews
• Cohort study
• Thematic analysis

Research bias

• Implicit bias
• Cognitive bias
• Survivorship bias
• Availability heuristic
• Nonresponse bias
• Regression to the mean

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Bevans, R. (2023, June 22). Hypothesis Testing | A Step-by-Step Guide with Easy Examples. Scribbr. Retrieved August 7, 2024, from https://www.scribbr.com/statistics/hypothesis-testing/

Is this article helpful?

Rebecca Bevans

Other students also liked, choosing the right statistical test | types & examples, understanding p values | definition and examples, what is your plagiarism score.

• Affiliate Program

• UNITED STATES
• 台灣 (TAIWAN)
• TÜRKIYE (TURKEY)
• Academic Editing Services
• - Research Paper
• - Journal Manuscript
• - Dissertation
• - College & University Assignments
• Admissions Editing Services
• - Application Essay
• - Personal Statement
• - Recommendation Letter
• - Cover Letter
• - CV/Resume
• Business Editing Services
• - Business Documents
• - Report & Brochure
• - Website & Blog
• Writer Editing Services
• - Script & Screenplay
• Our Editors
• Client Reviews
• Editing & Proofreading Prices
• Wordvice Points
• Partner Discount
• Plagiarism Checker
• APA Citation Generator
• MLA Citation Generator
• Chicago Citation Generator
• Vancouver Citation Generator
• - APA Style
• - MLA Style
• - Chicago Style
• - Vancouver Style
• Writing & Editing Guide
• Academic Resources
• Admissions Resources

How to Write a Research Hypothesis: Good & Bad Examples

What is a research hypothesis?

A research hypothesis is an attempt at explaining a phenomenon or the relationships between phenomena/variables in the real world. Hypotheses are sometimes called “educated guesses”, but they are in fact (or let’s say they should be) based on previous observations, existing theories, scientific evidence, and logic. A research hypothesis is also not a prediction—rather, predictions are ( should be) based on clearly formulated hypotheses. For example, “We tested the hypothesis that KLF2 knockout mice would show deficiencies in heart development” is an assumption or prediction, not a hypothesis. 

The research hypothesis at the basis of this prediction is “the product of the KLF2 gene is involved in the development of the cardiovascular system in mice”—and this hypothesis is probably (hopefully) based on a clear observation, such as that mice with low levels of Kruppel-like factor 2 (which KLF2 codes for) seem to have heart problems. From this hypothesis, you can derive the idea that a mouse in which this particular gene does not function cannot develop a normal cardiovascular system, and then make the prediction that we started with. 

What is the difference between a hypothesis and a prediction?

You might think that these are very subtle differences, and you will certainly come across many publications that do not contain an actual hypothesis or do not make these distinctions correctly. But considering that the formulation and testing of hypotheses is an integral part of the scientific method, it is good to be aware of the concepts underlying this approach. The two hallmarks of a scientific hypothesis are falsifiability (an evaluation standard that was introduced by the philosopher of science Karl Popper in 1934) and testability —if you cannot use experiments or data to decide whether an idea is true or false, then it is not a hypothesis (or at least a very bad one).

So, in a nutshell, you (1) look at existing evidence/theories, (2) come up with a hypothesis, (3) make a prediction that allows you to (4) design an experiment or data analysis to test it, and (5) come to a conclusion. Of course, not all studies have hypotheses (there is also exploratory or hypothesis-generating research), and you do not necessarily have to state your hypothesis as such in your paper. 

But for the sake of understanding the principles of the scientific method, let’s first take a closer look at the different types of hypotheses that research articles refer to and then give you a step-by-step guide for how to formulate a strong hypothesis for your own paper.

Types of Research Hypotheses

Hypotheses can be simple , which means they describe the relationship between one single independent variable (the one you observe variations in or plan to manipulate) and one single dependent variable (the one you expect to be affected by the variations/manipulation). If there are more variables on either side, you are dealing with a complex hypothesis. You can also distinguish hypotheses according to the kind of relationship between the variables you are interested in (e.g., causal or associative ). But apart from these variations, we are usually interested in what is called the “alternative hypothesis” and, in contrast to that, the “null hypothesis”. If you think these two should be listed the other way round, then you are right, logically speaking—the alternative should surely come second. However, since this is the hypothesis we (as researchers) are usually interested in, let’s start from there.

Alternative Hypothesis

If you predict a relationship between two variables in your study, then the research hypothesis that you formulate to describe that relationship is your alternative hypothesis (usually H1 in statistical terms). The goal of your hypothesis testing is thus to demonstrate that there is sufficient evidence that supports the alternative hypothesis, rather than evidence for the possibility that there is no such relationship. The alternative hypothesis is usually the research hypothesis of a study and is based on the literature, previous observations, and widely known theories. 

Null Hypothesis

The hypothesis that describes the other possible outcome, that is, that your variables are not related, is the null hypothesis ( H0 ). Based on your findings, you choose between the two hypotheses—usually that means that if your prediction was correct, you reject the null hypothesis and accept the alternative. Make sure, however, that you are not getting lost at this step of the thinking process: If your prediction is that there will be no difference or change, then you are trying to find support for the null hypothesis and reject H1. 

Directional Hypothesis

While the null hypothesis is obviously “static”, the alternative hypothesis can specify a direction for the observed relationship between variables—for example, that mice with higher expression levels of a certain protein are more active than those with lower levels. This is then called a one-tailed hypothesis. 

Another example for a directional one-tailed alternative hypothesis would be that 

H1: Attending private classes before important exams has a positive effect on performance. 

Your null hypothesis would then be that

H0: Attending private classes before important exams has no/a negative effect on performance.

Nondirectional Hypothesis

A nondirectional hypothesis does not specify the direction of the potentially observed effect, only that there is a relationship between the studied variables—this is called a two-tailed hypothesis. For instance, if you are studying a new drug that has shown some effects on pathways involved in a certain condition (e.g., anxiety) in vitro in the lab, but you can’t say for sure whether it will have the same effects in an animal model or maybe induce other/side effects that you can’t predict and potentially increase anxiety levels instead, you could state the two hypotheses like this:

H1: The only lab-tested drug (somehow) affects anxiety levels in an anxiety mouse model.

You then test this nondirectional alternative hypothesis against the null hypothesis:

H0: The only lab-tested drug has no effect on anxiety levels in an anxiety mouse model.

How to Write a Hypothesis for a Research Paper

Now that we understand the important distinctions between different kinds of research hypotheses, let’s look at a simple process of how to write a hypothesis.

Writing a Hypothesis Step:1

Ask a question, based on earlier research. Research always starts with a question, but one that takes into account what is already known about a topic or phenomenon. For example, if you are interested in whether people who have pets are happier than those who don’t, do a literature search and find out what has already been demonstrated. You will probably realize that yes, there is quite a bit of research that shows a relationship between happiness and owning a pet—and even studies that show that owning a dog is more beneficial than owning a cat ! Let’s say you are so intrigued by this finding that you wonder: 

What is it that makes dog owners even happier than cat owners? 

Let’s move on to Step 2 and find an answer to that question.

Writing a Hypothesis Step 2:

Formulate a strong hypothesis by answering your own question. Again, you don’t want to make things up, take unicorns into account, or repeat/ignore what has already been done. Looking at the dog-vs-cat papers your literature search returned, you see that most studies are based on self-report questionnaires on personality traits, mental health, and life satisfaction. What you don’t find is any data on actual (mental or physical) health measures, and no experiments. You therefore decide to make a bold claim come up with the carefully thought-through hypothesis that it’s maybe the lifestyle of the dog owners, which includes walking their dog several times per day, engaging in fun and healthy activities such as agility competitions, and taking them on trips, that gives them that extra boost in happiness. You could therefore answer your question in the following way:

Dog owners are happier than cat owners because of the dog-related activities they engage in.

Now you have to verify that your hypothesis fulfills the two requirements we introduced at the beginning of this resource article: falsifiability and testability . If it can’t be wrong and can’t be tested, it’s not a hypothesis. We are lucky, however, because yes, we can test whether owning a dog but not engaging in any of those activities leads to lower levels of happiness or well-being than owning a dog and playing and running around with them or taking them on trips.  

Writing a Hypothesis Step 3:

Make your predictions and define your variables. We have verified that we can test our hypothesis, but now we have to define all the relevant variables, design our experiment or data analysis, and make precise predictions. You could, for example, decide to study dog owners (not surprising at this point), let them fill in questionnaires about their lifestyle as well as their life satisfaction (as other studies did), and then compare two groups of active and inactive dog owners. Alternatively, if you want to go beyond the data that earlier studies produced and analyzed and directly manipulate the activity level of your dog owners to study the effect of that manipulation, you could invite them to your lab, select groups of participants with similar lifestyles, make them change their lifestyle (e.g., couch potato dog owners start agility classes, very active ones have to refrain from any fun activities for a certain period of time) and assess their happiness levels before and after the intervention. In both cases, your independent variable would be “ level of engagement in fun activities with dog” and your dependent variable would be happiness or well-being . 

Examples of a Good and Bad Hypothesis

Let’s look at a few examples of good and bad hypotheses to get you started.

Good Hypothesis Examples

Bad Hypothesis Examples

Tips for Writing a Research Hypothesis

If you understood the distinction between a hypothesis and a prediction we made at the beginning of this article, then you will have no problem formulating your hypotheses and predictions correctly. To refresh your memory: We have to (1) look at existing evidence, (2) come up with a hypothesis, (3) make a prediction, and (4) design an experiment. For example, you could summarize your dog/happiness study like this:

(1) While research suggests that dog owners are happier than cat owners, there are no reports on what factors drive this difference. (2) We hypothesized that it is the fun activities that many dog owners (but very few cat owners) engage in with their pets that increases their happiness levels. (3) We thus predicted that preventing very active dog owners from engaging in such activities for some time and making very inactive dog owners take up such activities would lead to an increase and decrease in their overall self-ratings of happiness, respectively. (4) To test this, we invited dog owners into our lab, assessed their mental and emotional well-being through questionnaires, and then assigned them to an “active” and an “inactive” group, depending on… 

Note that you use “we hypothesize” only for your hypothesis, not for your experimental prediction, and “would” or “if – then” only for your prediction, not your hypothesis. A hypothesis that states that something “would” affect something else sounds as if you don’t have enough confidence to make a clear statement—in which case you can’t expect your readers to believe in your research either. Write in the present tense, don’t use modal verbs that express varying degrees of certainty (such as may, might, or could ), and remember that you are not drawing a conclusion while trying not to exaggerate but making a clear statement that you then, in a way, try to disprove . And if that happens, that is not something to fear but an important part of the scientific process.

Similarly, don’t use “we hypothesize” when you explain the implications of your research or make predictions in the conclusion section of your manuscript, since these are clearly not hypotheses in the true sense of the word. As we said earlier, you will find that many authors of academic articles do not seem to care too much about these rather subtle distinctions, but thinking very clearly about your own research will not only help you write better but also ensure that even that infamous Reviewer 2 will find fewer reasons to nitpick about your manuscript. 

Perfect Your Manuscript With Professional Editing

Now that you know how to write a strong research hypothesis for your research paper, you might be interested in our free AI Proofreader , Wordvice AI, which finds and fixes errors in grammar, punctuation, and word choice in academic texts. Or if you are interested in human proofreading , check out our English editing services , including research paper editing and manuscript editing .

On the Wordvice academic resources website , you can also find many more articles and other resources that can help you with writing the other parts of your research paper , with making a research paper outline before you put everything together, or with writing an effective cover letter once you are ready to submit.

• Bipolar Disorder
• Therapy Center
• When To See a Therapist
• Types of Therapy
• Best Online Therapy
• Best Couples Therapy
• Best Family Therapy
• Managing Stress
• Sleep and Dreaming
• Understanding Emotions
• Self-Improvement
• Healthy Relationships
• Student Resources
• Personality Types
• Sweepstakes
• Guided Meditations
• Verywell Mind Insights
• 2024 Verywell Mind 25
• Mental Health in the Classroom
• Editorial Process
• Meet Our Review Board
• Crisis Support

How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when  conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a  correlational study  can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses .  R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:].  Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ?  PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies .  Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Educational resources and simple solutions for your research journey

What is a Research Hypothesis: How to Write it, Types, and Examples

Any research begins with a research question and a research hypothesis . A research question alone may not suffice to design the experiment(s) needed to answer it. A hypothesis is central to the scientific method. But what is a hypothesis ? A hypothesis is a testable statement that proposes a possible explanation to a phenomenon, and it may include a prediction. Next, you may ask what is a research hypothesis ? Simply put, a research hypothesis is a prediction or educated guess about the relationship between the variables that you want to investigate.  

It is important to be thorough when developing your research hypothesis. Shortcomings in the framing of a hypothesis can affect the study design and the results. A better understanding of the research hypothesis definition and characteristics of a good hypothesis will make it easier for you to develop your own hypothesis for your research. Let’s dive in to know more about the types of research hypothesis , how to write a research hypothesis , and some research hypothesis examples .  

Table of Contents

What is a hypothesis ?  

A hypothesis is based on the existing body of knowledge in a study area. Framed before the data are collected, a hypothesis states the tentative relationship between independent and dependent variables, along with a prediction of the outcome.  

What is a research hypothesis ?  

Young researchers starting out their journey are usually brimming with questions like “ What is a hypothesis ?” “ What is a research hypothesis ?” “How can I write a good research hypothesis ?”   

A research hypothesis is a statement that proposes a possible explanation for an observable phenomenon or pattern. It guides the direction of a study and predicts the outcome of the investigation. A research hypothesis is testable, i.e., it can be supported or disproven through experimentation or observation.     

Characteristics of a good hypothesis  

Here are the characteristics of a good hypothesis :  

• Clearly formulated and free of language errors and ambiguity  
• Concise and not unnecessarily verbose  
• Has clearly defined variables  
• Testable and stated in a way that allows for it to be disproven  
• Can be tested using a research design that is feasible, ethical, and practical   
• Specific and relevant to the research problem  
• Rooted in a thorough literature search  
• Can generate new knowledge or understanding.  

How to create an effective research hypothesis  

A study begins with the formulation of a research question. A researcher then performs background research. This background information forms the basis for building a good research hypothesis . The researcher then performs experiments, collects, and analyzes the data, interprets the findings, and ultimately, determines if the findings support or negate the original hypothesis.  

Let’s look at each step for creating an effective, testable, and good research hypothesis :  

• Identify a research problem or question: Start by identifying a specific research problem.   
• Review the literature: Conduct an in-depth review of the existing literature related to the research problem to grasp the current knowledge and gaps in the field.   
• Formulate a clear and testable hypothesis : Based on the research question, use existing knowledge to form a clear and testable hypothesis . The hypothesis should state a predicted relationship between two or more variables that can be measured and manipulated. Improve the original draft till it is clear and meaningful.  
• State the null hypothesis: The null hypothesis is a statement that there is no relationship between the variables you are studying.   
• Define the population and sample: Clearly define the population you are studying and the sample you will be using for your research.  
• Select appropriate methods for testing the hypothesis: Select appropriate research methods, such as experiments, surveys, or observational studies, which will allow you to test your research hypothesis .  

Remember that creating a research hypothesis is an iterative process, i.e., you might have to revise it based on the data you collect. You may need to test and reject several hypotheses before answering the research problem.  

How to write a research hypothesis  

When you start writing a research hypothesis , you use an “if–then” statement format, which states the predicted relationship between two or more variables. Clearly identify the independent variables (the variables being changed) and the dependent variables (the variables being measured), as well as the population you are studying. Review and revise your hypothesis as needed.  

An example of a research hypothesis in this format is as follows:  

“ If [athletes] follow [cold water showers daily], then their [endurance] increases.”  

Population: athletes  

Independent variable: daily cold water showers  

Dependent variable: endurance  

You may have understood the characteristics of a good hypothesis . But note that a research hypothesis is not always confirmed; a researcher should be prepared to accept or reject the hypothesis based on the study findings.  

Research hypothesis checklist  

Following from above, here is a 10-point checklist for a good research hypothesis :  

• Testable: A research hypothesis should be able to be tested via experimentation or observation.  
• Specific: A research hypothesis should clearly state the relationship between the variables being studied.  
• Based on prior research: A research hypothesis should be based on existing knowledge and previous research in the field.  
• Falsifiable: A research hypothesis should be able to be disproven through testing.  
• Clear and concise: A research hypothesis should be stated in a clear and concise manner.  
• Logical: A research hypothesis should be logical and consistent with current understanding of the subject.  
• Relevant: A research hypothesis should be relevant to the research question and objectives.  
• Feasible: A research hypothesis should be feasible to test within the scope of the study.  
• Reflects the population: A research hypothesis should consider the population or sample being studied.  
• Uncomplicated: A good research hypothesis is written in a way that is easy for the target audience to understand.  

By following this research hypothesis checklist , you will be able to create a research hypothesis that is strong, well-constructed, and more likely to yield meaningful results.  

Types of research hypothesis  

Different types of research hypothesis are used in scientific research:  

1. Null hypothesis:

A null hypothesis states that there is no change in the dependent variable due to changes to the independent variable. This means that the results are due to chance and are not significant. A null hypothesis is denoted as H0 and is stated as the opposite of what the alternative hypothesis states.   

Example: “ The newly identified virus is not zoonotic .”  

2. Alternative hypothesis:

This states that there is a significant difference or relationship between the variables being studied. It is denoted as H1 or Ha and is usually accepted or rejected in favor of the null hypothesis.  

Example: “ The newly identified virus is zoonotic .”  

3. Directional hypothesis :

This specifies the direction of the relationship or difference between variables; therefore, it tends to use terms like increase, decrease, positive, negative, more, or less.   

Example: “ The inclusion of intervention X decreases infant mortality compared to the original treatment .”   

4. Non-directional hypothesis:

While it does not predict the exact direction or nature of the relationship between the two variables, a non-directional hypothesis states the existence of a relationship or difference between variables but not the direction, nature, or magnitude of the relationship. A non-directional hypothesis may be used when there is no underlying theory or when findings contradict previous research.  

Example, “ Cats and dogs differ in the amount of affection they express .”  

5. Simple hypothesis :

A simple hypothesis only predicts the relationship between one independent and another independent variable.  

Example: “ Applying sunscreen every day slows skin aging .”  

6 . Complex hypothesis :

A complex hypothesis states the relationship or difference between two or more independent and dependent variables.   

Example: “ Applying sunscreen every day slows skin aging, reduces sun burn, and reduces the chances of skin cancer .” (Here, the three dependent variables are slowing skin aging, reducing sun burn, and reducing the chances of skin cancer.)  

7. Associative hypothesis:  

An associative hypothesis states that a change in one variable results in the change of the other variable. The associative hypothesis defines interdependency between variables.  

Example: “ There is a positive association between physical activity levels and overall health .”  

8 . Causal hypothesis:

A causal hypothesis proposes a cause-and-effect interaction between variables.  

Example: “ Long-term alcohol use causes liver damage .”  

Note that some of the types of research hypothesis mentioned above might overlap. The types of hypothesis chosen will depend on the research question and the objective of the study.  

Research hypothesis examples  

Here are some good research hypothesis examples :  

“The use of a specific type of therapy will lead to a reduction in symptoms of depression in individuals with a history of major depressive disorder.”  

“Providing educational interventions on healthy eating habits will result in weight loss in overweight individuals.”  

“Plants that are exposed to certain types of music will grow taller than those that are not exposed to music.”  

“The use of the plant growth regulator X will lead to an increase in the number of flowers produced by plants.”  

Characteristics that make a research hypothesis weak are unclear variables, unoriginality, being too general or too vague, and being untestable. A weak hypothesis leads to weak research and improper methods.   

Some bad research hypothesis examples (and the reasons why they are “bad”) are as follows:  

“This study will show that treatment X is better than any other treatment . ” (This statement is not testable, too broad, and does not consider other treatments that may be effective.)  

“This study will prove that this type of therapy is effective for all mental disorders . ” (This statement is too broad and not testable as mental disorders are complex and different disorders may respond differently to different types of therapy.)  

“Plants can communicate with each other through telepathy . ” (This statement is not testable and lacks a scientific basis.)  

Importance of testable hypothesis  

If a research hypothesis is not testable, the results will not prove or disprove anything meaningful. The conclusions will be vague at best. A testable hypothesis helps a researcher focus on the study outcome and understand the implication of the question and the different variables involved. A testable hypothesis helps a researcher make precise predictions based on prior research.  

To be considered testable, there must be a way to prove that the hypothesis is true or false; further, the results of the hypothesis must be reproducible.  

Frequently Asked Questions (FAQs) on research hypothesis  

1. What is the difference between research question and research hypothesis ?  

A research question defines the problem and helps outline the study objective(s). It is an open-ended statement that is exploratory or probing in nature. Therefore, it does not make predictions or assumptions. It helps a researcher identify what information to collect. A research hypothesis , however, is a specific, testable prediction about the relationship between variables. Accordingly, it guides the study design and data analysis approach.

2. When to reject null hypothesis ?

A null hypothesis should be rejected when the evidence from a statistical test shows that it is unlikely to be true. This happens when the test statistic (e.g., p -value) is less than the defined significance level (e.g., 0.05). Rejecting the null hypothesis does not necessarily mean that the alternative hypothesis is true; it simply means that the evidence found is not compatible with the null hypothesis.  

3. How can I be sure my hypothesis is testable?  

A testable hypothesis should be specific and measurable, and it should state a clear relationship between variables that can be tested with data. To ensure that your hypothesis is testable, consider the following:  

• Clearly define the key variables in your hypothesis. You should be able to measure and manipulate these variables in a way that allows you to test the hypothesis.  
• The hypothesis should predict a specific outcome or relationship between variables that can be measured or quantified.   
• You should be able to collect the necessary data within the constraints of your study.  
• It should be possible for other researchers to replicate your study, using the same methods and variables.   
• Your hypothesis should be testable by using appropriate statistical analysis techniques, so you can draw conclusions, and make inferences about the population from the sample data.  
• The hypothesis should be able to be disproven or rejected through the collection of data.  

4. How do I revise my research hypothesis if my data does not support it?  

If your data does not support your research hypothesis , you will need to revise it or develop a new one. You should examine your data carefully and identify any patterns or anomalies, re-examine your research question, and/or revisit your theory to look for any alternative explanations for your results. Based on your review of the data, literature, and theories, modify your research hypothesis to better align it with the results you obtained. Use your revised hypothesis to guide your research design and data collection. It is important to remain objective throughout the process.  

5. I am performing exploratory research. Do I need to formulate a research hypothesis?  

As opposed to “confirmatory” research, where a researcher has some idea about the relationship between the variables under investigation, exploratory research (or hypothesis-generating research) looks into a completely new topic about which limited information is available. Therefore, the researcher will not have any prior hypotheses. In such cases, a researcher will need to develop a post-hoc hypothesis. A post-hoc research hypothesis is generated after these results are known.  

6. How is a research hypothesis different from a research question?

A research question is an inquiry about a specific topic or phenomenon, typically expressed as a question. It seeks to explore and understand a particular aspect of the research subject. In contrast, a research hypothesis is a specific statement or prediction that suggests an expected relationship between variables. It is formulated based on existing knowledge or theories and guides the research design and data analysis.

7. Can a research hypothesis change during the research process?

Yes, research hypotheses can change during the research process. As researchers collect and analyze data, new insights and information may emerge that require modification or refinement of the initial hypotheses. This can be due to unexpected findings, limitations in the original hypotheses, or the need to explore additional dimensions of the research topic. Flexibility is crucial in research, allowing for adaptation and adjustment of hypotheses to align with the evolving understanding of the subject matter.

8. How many hypotheses should be included in a research study?

The number of research hypotheses in a research study varies depending on the nature and scope of the research. It is not necessary to have multiple hypotheses in every study. Some studies may have only one primary hypothesis, while others may have several related hypotheses. The number of hypotheses should be determined based on the research objectives, research questions, and the complexity of the research topic. It is important to ensure that the hypotheses are focused, testable, and directly related to the research aims.

9. Can research hypotheses be used in qualitative research?

Yes, research hypotheses can be used in qualitative research, although they are more commonly associated with quantitative research. In qualitative research, hypotheses may be formulated as tentative or exploratory statements that guide the investigation. Instead of testing hypotheses through statistical analysis, qualitative researchers may use the hypotheses to guide data collection and analysis, seeking to uncover patterns, themes, or relationships within the qualitative data. The emphasis in qualitative research is often on generating insights and understanding rather than confirming or rejecting specific research hypotheses through statistical testing.

Editage All Access is a subscription-based platform that unifies the best AI tools and services designed to speed up, simplify, and streamline every step of a researcher’s journey. The Editage All Access Pack is a one-of-a-kind subscription that unlocks full access to an AI writing assistant, literature recommender, journal finder, scientific illustration tool, and exclusive discounts on professional publication services from Editage.  

Based on 22+ years of experience in academia, Editage All Access empowers researchers to put their best research forward and move closer to success. Explore our top AI Tools pack, AI Tools + Publication Services pack, or Build Your Own Plan. Find everything a researcher needs to succeed, all in one place –  Get All Access now starting at just $14 a month !    

Related Posts

What is IMRaD Format in Research?

What is a Review Article? How to Write it?

Holman Library

• GRC Holman Library
• Green River LibGuides

Research Guide: Scholarly Journals

• Introduction: Hypothesis/Thesis
• Why Use Scholarly Journals?
• What does "Peer-Reviewed" mean?
• What is *NOT* a Scholarly Journal Article?
• Interlibrary Loan for Journal Articles
• Reading the Citation
• Authors' Credentials
• Literature Review
• Methodology
• Results/Data
• Discussion/Conclusions
• APA Citations for Scholarly Journal Articles
• MLA Citations for Scholarly Journal Articles

Hypothesis or Thesis

Looking for the author's thesis or hypothesis.

The image below shows the part of the scholarly article that shows where the authors are making their argument. 

(click on image to enlarge)

• The first few paragraphs of a journal article serve to introduce the topic, to provide the author's hypothesis or thesis, and to indicate why the research was done.  
• A thesis or hypothesis is not always clearly labeled; you may need to read through the introductory paragraphs to determine what the authors are proposing.
• << Previous: How to Read a Scholarly Article
• Next: Reading the Citation >>
• Last Updated: Aug 6, 2024 3:04 PM
• URL: https://libguides.greenriver.edu/scholarlyjournals

How to Develop a Good Research Hypothesis

The story of a research study begins by asking a question. Researchers all around the globe are asking curious questions and formulating research hypothesis. However, whether the research study provides an effective conclusion depends on how well one develops a good research hypothesis. Research hypothesis examples could help researchers get an idea as to how to write a good research hypothesis.

This blog will help you understand what is a research hypothesis, its characteristics and, how to formulate a research hypothesis

Table of Contents

What is Hypothesis?

Hypothesis is an assumption or an idea proposed for the sake of argument so that it can be tested. It is a precise, testable statement of what the researchers predict will be outcome of the study.  Hypothesis usually involves proposing a relationship between two variables: the independent variable (what the researchers change) and the dependent variable (what the research measures).

What is a Research Hypothesis?

Research hypothesis is a statement that introduces a research question and proposes an expected result. It is an integral part of the scientific method that forms the basis of scientific experiments. Therefore, you need to be careful and thorough when building your research hypothesis. A minor flaw in the construction of your hypothesis could have an adverse effect on your experiment. In research, there is a convention that the hypothesis is written in two forms, the null hypothesis, and the alternative hypothesis (called the experimental hypothesis when the method of investigation is an experiment).

Characteristics of a Good Research Hypothesis

As the hypothesis is specific, there is a testable prediction about what you expect to happen in a study. You may consider drawing hypothesis from previously published research based on the theory.

A good research hypothesis involves more effort than just a guess. In particular, your hypothesis may begin with a question that could be further explored through background research.

To help you formulate a promising research hypothesis, you should ask yourself the following questions:

• Is the language clear and focused?
• What is the relationship between your hypothesis and your research topic?
• Is your hypothesis testable? If yes, then how?
• What are the possible explanations that you might want to explore?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate your variables without hampering the ethical standards?
• Does your research predict the relationship and outcome?
• Is your research simple and concise (avoids wordiness)?
• Is it clear with no ambiguity or assumptions about the readers’ knowledge
• Is your research observable and testable results?
• Is it relevant and specific to the research question or problem?

The questions listed above can be used as a checklist to make sure your hypothesis is based on a solid foundation. Furthermore, it can help you identify weaknesses in your hypothesis and revise it if necessary.

Source: Educational Hub

How to formulate a research hypothesis.

A testable hypothesis is not a simple statement. It is rather an intricate statement that needs to offer a clear introduction to a scientific experiment, its intentions, and the possible outcomes. However, there are some important things to consider when building a compelling hypothesis.

1. State the problem that you are trying to solve.

Make sure that the hypothesis clearly defines the topic and the focus of the experiment.

2. Try to write the hypothesis as an if-then statement.

Follow this template: If a specific action is taken, then a certain outcome is expected.

3. Define the variables

Independent variables are the ones that are manipulated, controlled, or changed. Independent variables are isolated from other factors of the study.

Dependent variables , as the name suggests are dependent on other factors of the study. They are influenced by the change in independent variable.

4. Scrutinize the hypothesis

Evaluate assumptions, predictions, and evidence rigorously to refine your understanding.

Types of Research Hypothesis

The types of research hypothesis are stated below:

1. Simple Hypothesis

It predicts the relationship between a single dependent variable and a single independent variable.

2. Complex Hypothesis

It predicts the relationship between two or more independent and dependent variables.

3. Directional Hypothesis

It specifies the expected direction to be followed to determine the relationship between variables and is derived from theory. Furthermore, it implies the researcher’s intellectual commitment to a particular outcome.

4. Non-directional Hypothesis

It does not predict the exact direction or nature of the relationship between the two variables. The non-directional hypothesis is used when there is no theory involved or when findings contradict previous research.

5. Associative and Causal Hypothesis

The associative hypothesis defines interdependency between variables. A change in one variable results in the change of the other variable. On the other hand, the causal hypothesis proposes an effect on the dependent due to manipulation of the independent variable.

6. Null Hypothesis

Null hypothesis states a negative statement to support the researcher’s findings that there is no relationship between two variables. There will be no changes in the dependent variable due the manipulation of the independent variable. Furthermore, it states results are due to chance and are not significant in terms of supporting the idea being investigated.

7. Alternative Hypothesis

It states that there is a relationship between the two variables of the study and that the results are significant to the research topic. An experimental hypothesis predicts what changes will take place in the dependent variable when the independent variable is manipulated. Also, it states that the results are not due to chance and that they are significant in terms of supporting the theory being investigated.

Research Hypothesis Examples of Independent and Dependent Variables

Research Hypothesis Example 1 The greater number of coal plants in a region (independent variable) increases water pollution (dependent variable). If you change the independent variable (building more coal factories), it will change the dependent variable (amount of water pollution).

Research Hypothesis Example 2 What is the effect of diet or regular soda (independent variable) on blood sugar levels (dependent variable)? If you change the independent variable (the type of soda you consume), it will change the dependent variable (blood sugar levels)

You should not ignore the importance of the above steps. The validity of your experiment and its results rely on a robust testable hypothesis. Developing a strong testable hypothesis has few advantages, it compels us to think intensely and specifically about the outcomes of a study. Consequently, it enables us to understand the implication of the question and the different variables involved in the study. Furthermore, it helps us to make precise predictions based on prior research. Hence, forming a hypothesis would be of great value to the research. Here are some good examples of testable hypotheses.

More importantly, you need to build a robust testable research hypothesis for your scientific experiments. A testable hypothesis is a hypothesis that can be proved or disproved as a result of experimentation.

Importance of a Testable Hypothesis

To devise and perform an experiment using scientific method, you need to make sure that your hypothesis is testable. To be considered testable, some essential criteria must be met:

• There must be a possibility to prove that the hypothesis is true.
• There must be a possibility to prove that the hypothesis is false.
• The results of the hypothesis must be reproducible.

Without these criteria, the hypothesis and the results will be vague. As a result, the experiment will not prove or disprove anything significant.

What are your experiences with building hypotheses for scientific experiments? What challenges did you face? How did you overcome these challenges? Please share your thoughts with us in the comments section.

Frequently Asked Questions

The steps to write a research hypothesis are: 1. Stating the problem: Ensure that the hypothesis defines the research problem 2. Writing a hypothesis as an 'if-then' statement: Include the action and the expected outcome of your study by following a ‘if-then’ structure. 3. Defining the variables: Define the variables as Dependent or Independent based on their dependency to other factors. 4. Scrutinizing the hypothesis: Identify the type of your hypothesis

Hypothesis testing is a statistical tool which is used to make inferences about a population data to draw conclusions for a particular hypothesis.

Hypothesis in statistics is a formal statement about the nature of a population within a structured framework of a statistical model. It is used to test an existing hypothesis by studying a population.

Research hypothesis is a statement that introduces a research question and proposes an expected result. It forms the basis of scientific experiments.

The different types of hypothesis in research are: • Null hypothesis: Null hypothesis is a negative statement to support the researcher’s findings that there is no relationship between two variables. • Alternate hypothesis: Alternate hypothesis predicts the relationship between the two variables of the study. • Directional hypothesis: Directional hypothesis specifies the expected direction to be followed to determine the relationship between variables. • Non-directional hypothesis: Non-directional hypothesis does not predict the exact direction or nature of the relationship between the two variables. • Simple hypothesis: Simple hypothesis predicts the relationship between a single dependent variable and a single independent variable. • Complex hypothesis: Complex hypothesis predicts the relationship between two or more independent and dependent variables. • Associative and casual hypothesis: Associative and casual hypothesis predicts the relationship between two or more independent and dependent variables. • Empirical hypothesis: Empirical hypothesis can be tested via experiments and observation. • Statistical hypothesis: A statistical hypothesis utilizes statistical models to draw conclusions about broader populations.

Wow! You really simplified your explanation that even dummies would find it easy to comprehend. Thank you so much.

Thanks a lot for your valuable guidance.

I enjoy reading the post. Hypotheses are actually an intrinsic part in a study. It bridges the research question and the methodology of the study.

Useful piece!

This is awesome.Wow.

It very interesting to read the topic, can you guide me any specific example of hypothesis process establish throw the Demand and supply of the specific product in market

Nicely explained

It is really a useful for me Kindly give some examples of hypothesis

It was a well explained content ,can you please give me an example with the null and alternative hypothesis illustrated

clear and concise. thanks.

So Good so Amazing

Good to learn

Thanks a lot for explaining to my level of understanding

Explained well and in simple terms. Quick read! Thank you

It awesome. It has really positioned me in my research project

Rate this article Cancel Reply

Your email address will not be published.

Enago Academy's Most Popular Articles

• Reporting Research

Choosing the Right Analytical Approach: Thematic analysis vs. content analysis for data interpretation

In research, choosing the right approach to understand data is crucial for deriving meaningful insights.…

Comparing Cross Sectional and Longitudinal Studies: 5 steps for choosing the right approach

The process of choosing the right research design can put ourselves at the crossroads of…

• Industry News

COPE Forum Discussion Highlights Challenges and Urges Clarity in Institutional Authorship Standards

The COPE forum discussion held in December 2023 initiated with a fundamental question — is…

• Career Corner

Unlocking the Power of Networking in Academic Conferences

Embarking on your first academic conference experience? Fear not, we got you covered! Academic conferences…

Research Recommendations – Guiding policy-makers for evidence-based decision making

Research recommendations play a crucial role in guiding scholars and researchers toward fruitful avenues of…

Choosing the Right Analytical Approach: Thematic analysis vs. content analysis for…

Comparing Cross Sectional and Longitudinal Studies: 5 steps for choosing the right…

How to Design Effective Research Questionnaires for Robust Findings

Sign-up to read more

Subscribe for free to get unrestricted access to all our resources on research writing and academic publishing including:

• 2000+ blog articles
• 50+ Webinars
• 10+ Expert podcasts
• 50+ Infographics
• 10+ Checklists
• Research Guides

We hate spam too. We promise to protect your privacy and never spam you.

• Publishing Research
• AI in Academia
• Promoting Research
• Diversity and Inclusion
• Infographics
• Expert Video Library
• Other Resources
• Enago Learn
• Upcoming & On-Demand Webinars
• Peer-Review Week 2023
• Open Access Week 2023
• Conference Videos
• Enago Report
• Journal Finder
• Enago Plagiarism & AI Grammar Check
• Editing Services
• Publication Support Services
• Research Impact
• Translation Services
• Publication solutions
• AI-Based Solutions
• Thought Leadership
• Call for Articles
• Call for Speakers
• Author Training
• Edit Profile

I am looking for Editing/ Proofreading services for my manuscript Tentative date of next journal submission:

In your opinion, what is the most effective way to improve integrity in the peer review process?

• Privacy Policy

Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Appendix in Research Paper – Examples and...

Research Contribution – Thesis Guide

Research Gap – Types, Examples and How to...

Critical Analysis – Types, Examples and Writing...

Future Research – Thesis Guide

Scope of the Research – Writing Guide and...

News tagged with hypothesis

• Date 6 hours 12 hours 1 day 3 days all
• Rank Last day 1 week 1 month all
• LiveRank Last day 1 week 1 month all
• Popular Last day 1 week 1 month all

Ammonites' fate sealed by meteor strike that wiped out dinosaurs

Ammonites were not in decline before their extinction, scientists have found.

Paleontology & Fossils

Jun 27, 2024

Extended maternal care is a central factor to animal and human longevity, modeling study suggests

The relationship between mother and child may offer clues to the mystery of why humans live longer lives than expected for their size—and shed new light on what it means to be human.

Jun 17, 2024

Schooling fish expend less energy in turbulent water compared to solitary swimmers, study finds

Swimming through turbulent water is easier for schooling fish compared to solitary swimmers, according to a study published June 6 in the open-access journal PLOS Biology by Yangfan Zhang of Harvard University, Massachusetts, ...

Plants & Animals

Jun 6, 2024

RNA's hidden potential: New study unveils its role in early life and future bioengineering

The beginning of life on Earth and its evolution over billions of years continue to intrigue researchers worldwide. The central dogma or the directional flow of genetic information from a deoxyribose nucleic acid (DNA) template ...

Molecular & Computational biology

Apr 18, 2024

Loathed by scientists, loved by nature: Sulfur and the origin of life

Many artists have tried to depict what Earth might have looked like billions of years ago, before life made its appearance. Many scenes trade snow-covered mountains for lava-gushing volcanoes and blue skies for lightning ...

Earth Sciences

Mar 13, 2024

Modeling the origins of life: New evidence for an 'RNA World'

Charles Darwin described evolution as "descent with modification." Genetic information in the form of DNA sequences is copied and passed down from one generation to the next. But this process must also be somewhat flexible, ...

Mar 4, 2024

Validating the low-rank hypothesis in complex systems

In a new study, scientists have investigated the pervasive low-rank hypothesis in complex systems, demonstrating that despite high-dimensional nonlinear dynamics, many real networks exhibit rapidly decreasing singular values, ...

General Physics

Jan 24, 2024

Cryo-microscopy reveals nano-sized copy machine implicated in origin of life

How the intricate molecular machinery of life arose from simple beginnings has been a long-standing question. Several lines of evidence point towards a primordial "RNA world," where an "RNA copy machine" (a so-called replicase) ...

Jan 16, 2024

Creating a welcoming and supportive environment helps immigrants better integrate, researcher says

Over the coming few years, hundreds of thousands of people are expected to come to Canada to work, study and settle. This year, the federal government has set a target of welcoming 485,000 new permanent residents.

Social Sciences

Jan 8, 2024

The 'longevity bottleneck' hypothesis: Research suggests that dinosaurs may have influenced how human beings age

Human aging may have been influenced by millions of years of dinosaur domination according to a new theory from a leading aging expert. The 'longevity bottleneck' hypothesis has been proposed by Professor Joao Pedro de Magalhaes ...

Nov 29, 2023

A hypothesis (from Greek ὑπόθεσις [iˈpoθesis]) consists either of a suggested explanation for an observable phenomenon or of a reasoned proposal predicting a possible causal correlation among multiple phenomena. The term derives from the Greek, hypotithenai meaning "to put under" or "to suppose." The scientific method requires that one can test a scientific hypothesis . Scientists generally base such hypotheses on previous observations or on extensions of scientific theories. Even though the words "hypothesis" and "theory" are often used synonymously in common and informal usage, a scientific hypothesis is not the same as a scientific theory . A hypothesis is never to be stated as a question, but always as a statement with an explanation following it. It is not to be a question because it states what the experimenter thinks will occur. Hypotheses are usually written in the "if-then form": If X, then Y.

This text uses material from Wikipedia , licensed under CC BY-SA

E-mail newsletter

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Indian J Crit Care Med
• v.23(Suppl 3); 2019 Sep

An Introduction to Statistics: Understanding Hypothesis Testing and Statistical Errors

Priya ranganathan.

1 Department of Anesthesiology, Critical Care and Pain, Tata Memorial Hospital, Mumbai, Maharashtra, India

2 Department of Surgical Oncology, Tata Memorial Centre, Mumbai, Maharashtra, India

The second article in this series on biostatistics covers the concepts of sample, population, research hypotheses and statistical errors.

How to cite this article

Ranganathan P, Pramesh CS. An Introduction to Statistics: Understanding Hypothesis Testing and Statistical Errors. Indian J Crit Care Med 2019;23(Suppl 3):S230–S231.

Two papers quoted in this issue of the Indian Journal of Critical Care Medicine report. The results of studies aim to prove that a new intervention is better than (superior to) an existing treatment. In the ABLE study, the investigators wanted to show that transfusion of fresh red blood cells would be superior to standard-issue red cells in reducing 90-day mortality in ICU patients. 1 The PROPPR study was designed to prove that transfusion of a lower ratio of plasma and platelets to red cells would be superior to a higher ratio in decreasing 24-hour and 30-day mortality in critically ill patients. 2 These studies are known as superiority studies (as opposed to noninferiority or equivalence studies which will be discussed in a subsequent article).

SAMPLE VERSUS POPULATION

A sample represents a group of participants selected from the entire population. Since studies cannot be carried out on entire populations, researchers choose samples, which are representative of the population. This is similar to walking into a grocery store and examining a few grains of rice or wheat before purchasing an entire bag; we assume that the few grains that we select (the sample) are representative of the entire sack of grains (the population).

The results of the study are then extrapolated to generate inferences about the population. We do this using a process known as hypothesis testing. This means that the results of the study may not always be identical to the results we would expect to find in the population; i.e., there is the possibility that the study results may be erroneous.

HYPOTHESIS TESTING

A clinical trial begins with an assumption or belief, and then proceeds to either prove or disprove this assumption. In statistical terms, this belief or assumption is known as a hypothesis. Counterintuitively, what the researcher believes in (or is trying to prove) is called the “alternate” hypothesis, and the opposite is called the “null” hypothesis; every study has a null hypothesis and an alternate hypothesis. For superiority studies, the alternate hypothesis states that one treatment (usually the new or experimental treatment) is superior to the other; the null hypothesis states that there is no difference between the treatments (the treatments are equal). For example, in the ABLE study, we start by stating the null hypothesis—there is no difference in mortality between groups receiving fresh RBCs and standard-issue RBCs. We then state the alternate hypothesis—There is a difference between groups receiving fresh RBCs and standard-issue RBCs. It is important to note that we have stated that the groups are different, without specifying which group will be better than the other. This is known as a two-tailed hypothesis and it allows us to test for superiority on either side (using a two-sided test). This is because, when we start a study, we are not 100% certain that the new treatment can only be better than the standard treatment—it could be worse, and if it is so, the study should pick it up as well. One tailed hypothesis and one-sided statistical testing is done for non-inferiority studies, which will be discussed in a subsequent paper in this series.

STATISTICAL ERRORS

There are two possibilities to consider when interpreting the results of a superiority study. The first possibility is that there is truly no difference between the treatments but the study finds that they are different. This is called a Type-1 error or false-positive error or alpha error. This means falsely rejecting the null hypothesis.

The second possibility is that there is a difference between the treatments and the study does not pick up this difference. This is called a Type 2 error or false-negative error or beta error. This means falsely accepting the null hypothesis.

The power of the study is the ability to detect a difference between groups and is the converse of the beta error; i.e., power = 1-beta error. Alpha and beta errors are finalized when the protocol is written and form the basis for sample size calculation for the study. In an ideal world, we would not like any error in the results of our study; however, we would need to do the study in the entire population (infinite sample size) to be able to get a 0% alpha and beta error. These two errors enable us to do studies with realistic sample sizes, with the compromise that there is a small possibility that the results may not always reflect the truth. The basis for this will be discussed in a subsequent paper in this series dealing with sample size calculation.

Conventionally, type 1 or alpha error is set at 5%. This means, that at the end of the study, if there is a difference between groups, we want to be 95% certain that this is a true difference and allow only a 5% probability that this difference has occurred by chance (false positive). Type 2 or beta error is usually set between 10% and 20%; therefore, the power of the study is 90% or 80%. This means that if there is a difference between groups, we want to be 80% (or 90%) certain that the study will detect that difference. For example, in the ABLE study, sample size was calculated with a type 1 error of 5% (two-sided) and power of 90% (type 2 error of 10%) (1).

Table 1 gives a summary of the two types of statistical errors with an example

Statistical errors

In the next article in this series, we will look at the meaning and interpretation of ‘ p ’ value and confidence intervals for hypothesis testing.

Source of support: Nil

Conflict of interest: None

A familiar culprit thought to be why depression affects motivation

Exercise’s positive effect on depression is well-known, but not how it produces its anti-depressant effects. After reviewing a broad range of studies into how depression and physical activity affect motivation, researchers have now come up with a hypothesis.

People suffering from depression can experience apathy, a lack of enthusiasm, and anhedonia, the inability to derive joy or pleasure from activities that they’d usually find pleasurable. Both contribute to a decrease in motivation that can prevent people from engaging in things like work and social activities.

It’s known that physical activity , especially aerobic exercise, can reduce or even prevent depressive symptoms, but how it does so remains unclear. Researchers from University College London (UCL) reviewed a broad range of human and animal studies that explored the mechanisms underpinning depression and exercise and have proposed a novel hypothesis for exercise’s antidepressant effects.

“The antidepressant effect of aerobic exercise has been convincingly demonstrated through randomized controlled trials, but its mechanism is not well understood,” said Dr Emily Hird from the Institute of Cognitive Neuroscience at UCL and the study’s lead and corresponding author. “This is, in part, because it likely involves a variety of biological and psychological processes."

Previous studies have suggested that anhedonia is associated with a disruption of the brain’s reward pathways. These pathways are activated whenever we experience something rewarding, like nice-tasting food, sex, or addictive drugs. When exposed to a rewarding stimulus, the brain responds by releasing more dopamine , which provides a sense of pleasure and the motivation to do something again because it feels good. The disruption to these pathways in depressed people means that they’re less willing – less motivated – to expend effort to receive a reward.

Research has also shown that depression, particularly anhedonia, is associated with inflammation and that inflammation, which is responsible for a wide range of medical conditions, disrupts dopamine activity. Studies have found that inhibiting the inflammatory chemicals the body produces reduces depressive symptoms and anhedonia. In summary, the evidence suggested that inflammation reduced dopamine transmission, which decreased motivation in response to the mental and/or physical effort required to achieve a reward, leading to anhedonia and fatigue. On the other hand, the researchers found good evidence that physical activity reduced inflammation and improved dopamine functioning, reward processing, effort exertion, and motivation.

“[A]longside its positive effect on inflammation, dopamine and reward processing, exercise also reduces oxidative stress and improves self-esteem and self-efficacy,” Hird said. “However, we are proposing that exercise – particularly aerobic activities that make you sweaty and out of breath – decreases inflammation and boosts dopamine transmission, which in turn increases the desire to exert effort, and therefore boosts motivation generally.”

The researchers hope their hypothesis will inform new treatment strategies for depression, like personalized exercise programs.

“Understanding the mechanisms that underly the antidepressant effects of physical activity in depression could also inform our understanding of the mechanisms causing depression and the development of novel intervention strategies, in particular personalized intervention and social prescribing,” said Hird.

Further study using large randomized controlled trials is needed to assess the effect of exercise on depression and measure its effect on variables including inflammation, dopamine transmission, and motivation. To that end, the researchers are running a trial involving 250 participants aged 18 to 60.

“Addressing barriers to exercise – particularly in people with depression – is crucial, as regular physical activity may be able to alleviate symptoms, enhance mood and empower individuals on their path to recovery,” said Hird. “As part of this, finding strategies to encourage exercise is key.”

The study was published in the journal Translational Psychiatry .

Source: UCL

Most Viewed

Turmeric and green tea among six supplements putting people in hospital, 1.7-lb hammock tent pitches in seconds, stands up to hurricane winds, cheap heat-storing 'firebricks' projected to save industries trillions.

• Open access
• Published: 02 August 2024

Expert consensus on multilevel implementation hypotheses to promote the uptake of youth care guidelines: a Delphi study

• Eveline M. Dubbeldeman   ORCID: orcid.org/0000-0003-1217-158X 1 ,
• Rianne M. J. J. van der Kleij 1 ,
• Evelyn A. Brakema 1 &
• Mathilde R. Crone 1 , 2  

Health Research Policy and Systems volume  22 , Article number:  89 ( 2024 ) Cite this article

104 Accesses

Metrics details

The implementation of youth care guidelines remains a complex process. Several evidence–based frameworks aid the identification and specification of implementation determinants and strategies. However, the influence of specific strategies on certain determinants remains unclear. Therefore, we need to clarify which active ingredients of strategies, known as behaviour change techniques (BCTs), elicit behaviour change and improve implementation outcomes. With this knowledge, we are able to formulate evidence–based implementation hypotheses. An implementation hypothesis details how determinants and in turn, implementation outcomes might be influenced by specific implementation strategies and their BCTs. We aimed to identify (1) determinants relevant to the implementation of youth care guidelines and (2) feasible and potentially effective implementation hypotheses.

A four–round online modified Delphi study was conducted. In the first round, experts rated the implementation determinants based on their relevance. Next, experts formulated implementation hypotheses by connecting BCTs and implementation strategies to determinants and were asked to provide a rationale for their choices. In round three, the experts reconsidered and finalised their hypotheses based on an anonymous overview of all formulated hypotheses, including rationales. Finally, the experts rated the implementation hypotheses based on their potential effectiveness and feasibility.

Fourteen experts completed the first, second, and third rounds, with 11 completed the final round. Guideline promotion, mandatory education, presence of an implementation leader, poor management support, knowledge regarding guideline use, and a lack of communication skills were reported as most relevant determinants. In total, 46 hypotheses were formulated, ranging from 6 to 9 per determinant. For each determinant, we provide an overview of the implementation hypotheses that were most commonly deemed feasible and potentially effective.

This study offers valuable insights into youth care guideline implementation by systematically identifying relevant determinants and formulating hypotheses based on expert input. Determinants related to engagement and to knowledge and skills were found to be relevant to youth care guideline implementation. This study offers a set of hypotheses that could help organisations, policymakers, and professionals guide the implementation process of youth care guidelines to ultimately improve implementation outcomes. The effectiveness of these hypotheses in practice remains to be assessed.

Peer Review reports

According to the Convention on the Rights of the Child [ 1 ], ‘All children must be able to grow up in a safe and healthy environment where there are plenty of opportunities, to develop as people and participate’. Unfortunately, not every child is given this opportunity, which raises worldwide concerns. In the Netherlands, the need for youth care has grown, with approximately 443 thousand children (10.0%) under 23 receiving youth care in 2019 [ 2 ], increasing to 467 thousand (10.6%) by 2022 [ 3 ]. Within the scope of this study, we defined youth care as the care provided for children and their families experiencing a variety of problems, such as parenting issues, adverse socioeconomic conditions, and psychosocial and stress–related problems [ 4 , 5 ]. For example, it supports families in financial hardship with education, healthcare, and nutrition, provides counselling and psychiatric help for adolescents with psychosocial issues, and protects children from domestic violence, offering them safe places and helping families tackle underlying issues. Untreated, these issues can hinder a child's development and lead to severe consequences like school dropout [ 6 ], antisocial or delinquent behaviour [ 6 , 7 , 8 ], severe psychological disorders [ 6 , 8 , 9 , 10 , 11 ], and child abuse [ 12 ]. Ensuring children receive adequate care is essential to safeguard their right to a secure and healthy upbringing, emphasized by the Convention on the Rights of the Child. This involves early detection of emotional, behavioural, and social problems, with professionals recognizing when to refer for specialist interventions. [ 13 ]. Early identification and treatment can mitigate problems later in life [ 14 , 15 , 16 ] and limit associated costs and risks to society [ 17 , 18 ].

In Dutch youth care, several evidence–based guidelines and interventions (further referred to as youth care guidelines) exist including the Model Protocol for Child Abuse and Domestic Violence [ 19 ], the Youth Health Care Guideline for Psychosocial Problems [ 20 ], and the Kindcheck [ 21 ]. These guidelines aid the identification and/or management of child psychosocial problems, child abuse and neglect (CAN), and parenting problems and to assist parents with mental health problems. For example, the Model Protocol for Domestic Violence and Child Abuse provides clear steps for professionals dealing with signs of violence. It involves identifying signs, consulting colleagues and reporting centers as needed, engaging with the individuals involved, assessing the situation, and deciding the appropriate action. This structured approach equips professionals with a comprehensive framework to effectively respond to signs of violence, ensuring the well-being of those affected [ 19 ]. However, the availability of evidence–based guidelines does guarantee their optimal implementation in practice [ 22 , 23 , 24 , 25 , 26 ]. Konijnendijk [ 24 ] showed that, despite professionals’ familiarity with the content of the CAN guidelines and their positive perceptions, full adherence was low. Similarly, a study evaluating guidelines on positive parenting and family violence prevention showed that while about half of the professionals were familiar with the guidelines, only 14–16% applied them in practice [ 25 ]. The implementation of guidelines poses inherent challenges, especially within youth care. The interdisciplinary nature of the field, combined with the need to address sensitive topics with vulnerable families, heightens the complexity. Additionally, challenges arise from growing waiting lists, increasing administrative burdens, and persistent personnel shortages within youth care [ 27 , 28 ]. Hence, research increasingly emphasises the implementation of guidelines and interventions. Various theoretical frameworks have been developed to guide and facilitate the implementation process, concentrating on determinants influencing implementation [ 29 , 30 , 31 ] and offering taxonomies for effective implementation strategies [ 32 ].

Studies have identified several determinants (i.e. barriers and facilitators) influencing the implementation of guidelines addressing CAN [ 24 , 33 , 34 , 35 , 36 ], domestic violence during pregnancy [ 35 , 37 , 38 ], shaken baby syndrome [ 39 ], and childhood obesity [ 40 ]. Common determinants across these guidelines include issues related to time [ 24 , 36 , 37 , 38 , 39 , 40 ] and knowledge [ 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ]. Barriers specific to CAN and domestic violence during pregnancy guidelines include professionals' concerns about their own [ 38 , 41 , 42 , 43 ] and/or patients’ safety [ 37 , 38 , 41 , 42 ]. Understanding the determinants related to a problem is essential as they offer valuable insights into developing effective implementation strategies. By identifying the root causes and contributing factors driving the problem, we can develop strategies that directly address these underlying issues, leading to more effective and sustainable solutions. However, despite providing valuable insights for developing strategies to optimize implementation, some determinants are challenging to change in practice, such as limited time and financial resources [ 44 ]. To ensure an effective implementation process, it is recommended to focus on determinants that are (1) important for guideline implementation and (2) changeable in practice (i.e., adjustable determinants) [ 45 ]. Determinants considered important and changeable are further referred to as relevant determinants (Box 1 ). Implementation strategies, such as local consensus discussions and the use of opinion leaders, aim to address these determinants and optimize implementation [ 32 ]. However, the specific impact of these strategies on determinants and their potential role in either implementation success or failure remains unclear. For example, strategies like educational outreach visits, learning collaboratives, and educational materials are considered effective in skill development [ 46 ]. Yet, the success of these strategies is not solely dependent on their direct impact on determinants. Embedded within strategies, Behaviour Change Techniques (BCTs), are specific techniques designed to induce behaviour change, playing a crucial role in shaping implementation outcomes. Examples of BCTs include providing instructions on how to perform behaviours, action planning, and using prompts or cues [ 47 ]. Despite the acknowledged effectiveness of these strategies and BCTs, the optimal combination that significantly influences implementation outcomes remains unclear. There is a need for a comprehensive understanding of how strategies and BCTs collectively influence determinants and, consequently, impact implementation performance [ 48 ]. With this knowledge, we are able to formulate detailed, evidence–based strategies that effectively stimulate the implementation of youth care guidelines.

Box 1: Relevant determinants and implementation hypotheses

We use the term relevant determinants to indicate those determinants that are (1) important for the implementation of youth care guidelines and (2) changeable in practice (i.e. adjustable to a large extent). An implementation hypothesis details how implementation determinants and implementation outcomes might be influenced by specific behavioral change techniques and implementation strategies.

In this study, we aimed to (1) identify the determinants most relevant to the implementation of youth care guidelines and (2) identify BCTs and combine them with implementation strategies to tackle barriers and strengthen facilitators. The present paper outlines a modified Delphi study designed to provide an overview of experts’ opinions on relevant determinants and feasible and potentially effective BCTs and implementation strategies. In selecting the Delphi study as our methodology, we recognize the importance of professionals’ expertise in the field of youth care implementation, providing a unique combination of theoretical knowledge, practical experience, and contextual awareness [ 49 , 50 ]. The involvement of experts in a systematic and iterative process, allows us to draw upon their diverse perspectives, fostering a collaborative approach that is essential for addressing the challenges in the implementation of youth care guidelines. The theory–informed behaviour change (TIBC) method developed by French et al. [ 51 ] guided our Delphi study (Table  1 ). The TIBC method is a systematic approach for developing implementation interventions designed to change professionals’ behaviour based on theoretical frameworks, empirical evidence, and practical considerations. The executing of the second and third steps of the TIBC method formed the foundation of our Delphi study. This method significantly contributes to our study objectives by providing a structured and theory-based framework to (1) identify determinants and (2) intervention components (i.e., BCTs and implementation strategies) that might be effective in addressing these determinants. In line with the work by French et al. [ 46 ], we use the term ‘implementation hypotheses’ to detail how specific BCTs and implementation strategies might influence implementation determinants and implementation outcomes (Box 1 ).

Study design

This study employed a four–round Delphi method. Relevant determinants were identified in a single round, guided by the primary objective of quickly obtaining experts' opinions on the relevance of determinants influencing the implementation of youth care guidelines. The formulation of implementation hypotheses involved a more nuanced and iterative process, spanning three rounds. This multi-round design aimed to harness the collective expertise of the participants and attain a nuanced understanding of their perspective on implementation hypotheses. The Delphi method, known for gathering participants' opinions within their field of expertise, offers advantages such as expert anonymity, iteration with controlled feedback, and statistical aggregation of group responses. It minimizes irrelevant discussions and group pressure towards conformity [ 49 , 50 ]. Because current information on implementation hypotheses is scarce, our goal was to obtain a hierarchical overview of experts' opinions on feasible and potentially effective hypotheses to influence implementation determinants, rather than striving for complete consensus. The Delphi study proved suitable for providing such information. Furthermore, the formulation of final hypotheses required multiple rounds and the use of embedded data, which is not feasible with a single questionnaire. We aimed to include experts in implementation research and practice-based experts, anticipating differing opinions from various perspectives. The Delphi method minimizes group effects, such as pressure and suppressed dissenting opinions, which might occur in focus groups.

Online questionnaires were developed using Qualtrics [ 52 ], a web–based survey tool, and were administered over a four–month period (September–December 2020). Figure  1 provides an overview of the Delphi study, including example questions. Our reporting adheres to the Conducting and REporting of DElphi Studies (CREDES) recommendations [ 53 ] (Additional file 1 ). Research involving health professionals completing a questionnaire on the use of guidelines falls outside the scope of the Medical Research Involving Human Subjects Act (WMO) in the Netherlands [ 54 ], making ethical approval unnecessary. Nonetheless, participants were well-informed about the study's objectives, the commitment to participant anonymity during interactions, and the assurance of anonymity in publishing study outcomes, aligning with Delphi method principles. Complete anonymity to researchers posed challenges due to practical considerations, such as reminding participants to complete questionnaires and using embedded data between rounds, necessitating knowledge of participant identities. Despite these challenges, we prioritized ethical practices to ensure participants' voluntary and well-informed involvement.

Overview of the four different rounds of this Delphi study

Preparation

The second step of the TIBC method involves identifying determinants that need addressing. In our approach, we conducted a modified Delphi study where the first round commenced with closed-ended questions on implementation determinants, deviating from the classical method that starts with open-ended questions [ 55 ]. This allowed us to present experts with a solid foundation of preselected determinants based on prior empirical research, minimizing their workload. Prior to the Delphi study, we conducted a systematic review to identify determinants influencing the implementation of youth care guidelines in general [ 56 ]. Additionally, non-published data on the implementation of a Dutch youth care guideline, Kindcheck, was utilized to identify determinants specific to this guideline. Based on these findings, we drafted a preliminary set of determinants, formulating them using the Consolidated Framework for Implementation Research (CFIR) developed by Damschroder [ 30 ]. The CFIR is an evidence–based framework that provides 39 constructs (i.e. determinants) arranged across five domains associated with effective implementation. Widely used in implementation research, the CFIR facilitates the practical application of results. In total, we identified 44 determinants, with 20 categorized as barriers and 24 as facilitators.

Round 1 – Rank the preselected determinants

Participants and procedures.

In the first round, our objective was to establish a ranking of determinants influencing the implementation of youth care guidelines based on experts' opinions regarding their relevance. We recruited experts in the field of youth care guidelines in the Netherlands through convenience sampling within the research network and snowball sampling. Potential participants received information about the study via mail and were invited to participate. Those who agreed received an email containing the link to the initial questionnaire. Reminders were sent to non-responders after one and two weeks.

Questionnaire

The questionnaire comprised questions on the importance and changeability of 44 determinants influencing youth care guideline implementation (a total of 88 questions). Experts were asked to rate each determinant on a 10-point Likert scale, ranging from 1 = not important to 10 = very important. The level of changeability was rated on a 5-point Likert scale, ranging from 1 = not changeable to 5 = very changeable. To minimize availability bias, we provided experts with determinant-specific results from the systematic review [ 56 ] and the Kindcheck implementation study. Availability bias is a mental shortcut leading individuals to draw conclusions based on readily available examples; if something is easily and quickly recalled, it may be perceived as important [ 57 ].

To identify the determinants considered by the experts as the most relevant, we calculated median scores as indicators of the determinants’ importance and changeability. Following the approach by van Stralen et al. [ 58 ], determinants with a median score above 8 on a 10-point Likert scale were deemed important, while those with a median score of 4 on a 5-point Likert scale were considered changeable. In the first round, 19 determinants were identified as both important and changeable for the implementation of youth care guidelines. To avoid burdening the experts in the formulation of implementation hypotheses for too many determinants in the next round, determinants with a median score above 8 for importance were considered relevant. Mean scores were also considered, with determinants having a median score above 8 and a mean score of 7.51 or higher for importance, along with a median score of 4 or higher and a mean score of 3.60 or higher for changeability, being identified as the most relevant based on the grand mean of all determinants. These selected determinants served as inputs for the second round of this Delphi study.

Round 2 – Formulate implementation hypotheses

In the second round, implementation hypotheses were formulated for each determinant resulting from Round 1. We used the term ‘implementation hypotheses’ to detail how the implementation determinants and implementation outcomes might be influenced by implementation strategies and their BCTs.

Following the TIBC method, we asked the experts to match determinants with (1) a BCT formulated by Michie [ 59 ] and (2) an implementation strategy identified in the Expert Recommendations for Implementing Change (ERIC) project by Powell et al. [ 32 ].

In the first step, determinants were matched with BCTs. Experts were instructed to align each determinant with a feasible and potentially effective BCT [ 59 ]. To aid this process, a preselected list of effective BCTs for each specific determinant was provided, drawing from a recently conducted literature synthesis and expert consensus study on links between determinants and BCTs [ 48 , 60 ]. This approach aligns with the TIBC method, which recommends reviewing relevant literature to identify BCTs with a positive impact on the determinants in question.

The second step involved adding implementation strategies. Since the initial matches did not specify how BCTs could be practically delivered, experts were asked to include an implementation strategy from the ERIC project [ 32 , 61 ]. The ERIC project offers a compilation of 73 strategies clustered into 9 categories (e.g., engage consumers, develop stakeholder interrelationships, train and educate stakeholders, etc.) to facilitate the implementation of innovations. This step led to the formulation of an implementation hypothesis. Initially, experts were provided with a preselected list of effective implementation strategies based on literature, as compiled by Waltz and colleagues [ 46 ]. After completing both steps, experts were prompted to elaborate on the rationale for their choices using an open-ended question. Figure  2 provides an overview of the process for formulating implementation hypotheses. To present the formulated hypotheses per determinant, frequency tables were employed.

Process of implementation hypotheses formulation

Round 3 – Reconsider and finalise the implementation hypotheses

The third round was conducted to reconsider and finalise the implementation hypotheses formulated in the second round.

In the final round, participants were given an anonymous overview of hypotheses formulated by all experts for each determinant, along with the rationales for these hypotheses. Individual experts were then prompted to reconsider their initially chosen implementation hypotheses based on this collective overview. Experts had the option to either retain their own formulated implementation hypothesis or select one proposed by another expert. Frequency tables were utilized to list the formulated hypotheses for each determinant.

Round 4 – Rate the implementation hypotheses

In the last round, the experts indicated which implementation hypotheses they considered most feasible and potentially effective in addressing specific determinants.

Our research aimed to establish a hierarchical order for potentially effective implementation hypotheses. employing a ranking-type Delphi method with the fixed-sum approach [ 62 , 63 ]. Experts were asked to rate implementation hypotheses for each determinant by allocating a total of 100 points (either in full or in part) to the hypotheses formulated in Round 3. This method facilitated data analysis through simple parametric tests, such as average points and standard deviation. Experts had flexibility in distributing points, with the only exception being that they could allocate the most points to only one hypothesis. The web-based survey tool ensured that experts could proceed to the next question only if they had allocated exactly 100 points. Data analysis was conducted using IBM SPSS Statistics version 24.

Expert panel

In total, we approached 25 experts, and 19 expressed interest in participating. Fourteen experts completed the first, second, and third rounds (56% response rate), and 11 participated in the final round. All experts were Dutch and were experienced in guideline implementation: three had practice-based experience, and eleven had research-based experience in (youth care) guideline implementation (Additional file 2 ).

The results from Round 1 are presented in Additional file 3 . The determinants considered most important to youth care guideline implementation by experts included poor management support (median = 9.00; mean = 8.64), a lack of communication skills (median = 9.00; mean = 8.43), the presence of a motivated implementation leader (median = 9.00; mean = 8.86), and professionals’ belief in positive outcomes for the child (median = 9.00; mean = 8.64). Perceived as least important were professionals’ fear of making a false identification (median = 6.00; mean = 6.07), a lack of equipment (median = 6.50; mean = 6.21), low confidence in follow–up care by external organisations (median = 6.00; mean = 6.29), and the opportunity for professionals to make an anonymous call to external organisations (median = 6.00; mean = 5.57).

Regarding changeability, the determinants indicated as most changeable included poor procedural clarity (median = 4.50; median = 4.36), mandatory education (median = 4.50; mean = 4.43), guideline promotion (median = 4.50; mean = 4.36), and the presence of a motivated implementation leader (median = 4.50; mean = 4.29). The determinants identified by the experts as least changeable were a lack of time (median = 2.00; mean = 2.64), poor congruence in the current workflow (median = 2.00; mean = 2.71), professionals' fear of harming the relationship with their client (median = 2.50; mean = 2.86), and availability of time (median = 2.00; mean = 2.57).

Six determinants were identified as the most relevant for youth care guideline implementation, serving as the basis for the second round (Table  2 ). Organizing these determinants based on their alignment within CFIR constructs, they were categorized into two groups: (1) engagement (i.e., guideline promotion, mandatory education, presence of a motivated implementation leader, and poor management support) and (2) knowledge and skills (i.e., guideline knowledge and poor communication skills).

Rounds 2 and 3

In Round 2, a total of 60 different implementation hypotheses were formulated, with each determinant having between 9 to 11 different hypotheses. After the experts reevaluated their hypotheses based on anonymous feedback from other experts, a total of 46 hypotheses were formulated in Round 3, ranging from 6 to 9 different hypotheses per determinant (Additional file 3 ). Table 3 offers an overview of the two main implementation hypotheses most frequently considered by experts as feasible and potentially effective.

To facilitate change in determinants related to engagement, the BCT practical support was predominantly considered feasible and potentially effective ( n  = 10, 26.8%, Additional file 3 ). Additionally, various strategies for developing stakeholder interrelationships, such as using advisory boards and workgroups, obtaining formal commitments, and involving executive boards, were widely viewed as feasible and potentially effective in addressing determinants in practice ( n  = 35, 62.5%).

Knowledge and skills

Providing instructions on how to perform a behaviour was largely considered a feasible and potentially effective BCT to address knowledge about guideline use ( n  = 6, 42.9%). In tackling the lack of communication skills, behavioural practice/rehearsal was deemed feasible and potentially effective by the majority ( n  = 11, 78.6%). Various implementation strategies for training and educating stakeholders were most frequently considered feasible and potentially effective in addressing knowledge and skills in practice ( n  = 26, 92.9%).

We compiled a list of hypotheses ranked by the average points each implementation hypothesis received (Table  4 ). Figure  3 provides a simplified overview of the potential implementation hypotheses for each determinant based on the highest average points regarding their feasibility and potential effectiveness, as evaluated by the experts.

Overview of the determinants considered by the experts the most important and changeable for the implementation of youth care guidelines and possible strategies to address these determinants

Across the hypotheses, the highest number of points were allocated to the BCTs practical support and action planning (1183, 26.9% and 1118, 25.4%, respectively) and diverse implementation strategies regarding the development of stakeholder interrelationships (2246, 51.0%). Specifically, for guideline promotion, the hypotheses ‘detailed action plan to promote guideline use that should be discussed and revised during collaborative learning sessions’ received the most points (266, 24.2%).

“This takes time and congruence with all other guidelines. Prevent single mindedness (living one guideline....).Make teams responsible for their aptness to understand and practice all guidelines in an integrated and continuous way!” [respondent 8]

For organizing mandatory education, experts allocated the highest number of points to the following hypothesis: ‘detailed action plan in which the necessities, barriers, and facilitators for the organization of mandatory education, as well as professionals’ readiness to mandatory education, are taken into account’ (235, 21.4%).

“Involve the students in the planning and create time and space when necessary. Send a tailor out don't use a one size fits all approach. That does not motivate and does not work!” [respondent 8]

The hypothesis ‘provide implementation leaders ongoing consultations on how to perform their tasks and keep them motivated’ received the highest number of points for its potential to enhance the presence of a motivated implementation leader (250, 22.7%).

“My experience is that a the effort and the energy of the implantation leader is a reflection for those who have to implement, like a mirror” [respondent 2]

‘Obtaining formal commitments from, for example, guideline developers, that states they will provide management practical support on how to support professionals in using guidelines’ received the most points to address poor management support (277, 25.2%).

“Commitment written down may help professionals to talk to their managers regarding their responsibilities.” [respondent 5]

Across hypotheses, the experts allocated the highest number of points to the BCTs behavioural practice/rehearsal and instructions on how to perform a behaviour (851, 38.7% and 464, 21.7%, respectively) and various implementation strategies regarding the training and education of stakeholders (1965, 89.3%). More specifically, the hypothesis ‘providing instructions to professionals during collaborative learning sessions’ was mostly considered feasible and potentially effective in facilitating knowledge transfer regarding guideline use (242, 22.0%).

“If specific knowledge is required it is needed that people can actually obtain that knowledge and so I chose the technique that deals with that knowledge, by creating a learning collaborative you have a structured and sustainable way of improving people's knowledge. You can share materials etc. within that group to foster learning.” [respondent 10]

To address the lack of communication skills, most of the experts considered the hypothesis ‘behavioural practice/rehearsal during educational outreach visits’ to be feasible and potentially effective (246, 22.4%).

“I think communication skills can be best obtained by training. It is difficult to get professionals together for training skills, so educational outreach visit seemed the most feasible. However, also in these trainings videos of ' good behaviour' can be used to discuss how you can communicate about certain diagnosis, treatments and so on.” [respondent 11].

Our objective was to identify the relevant determinants of youth care guideline implementation and formulate feasible and potentially effective implementation hypotheses to address these determinants. The recruited experts identified determinants related to 1) engagement (i.e., guideline promotion, mandatory education, the presence of a motivated implementation leader, and management support) and 2) knowledge and skills (i.e., guideline knowledge and communication skills) as crucial for the implementation of guidelines.

To elicit changes in determinants relating to engagement, the BCTs practical support and action planning were predominantly considered feasible and potentially effective. Various implementation strategies aimed at developing stakeholder interrelationships were most frequently regarded as feasible and potentially effective in facilitating changes in practice. To elicit changes in professionals’ knowledge and skills, the BCTs providing instructions on guideline use and behavioural practice/rehearsal practice were predominantly considered feasible and potentially effective. The majority of experts deemed various strategies focused on training and educating stakeholders feasible and potentially effective in facilitating changes in practice.

In total, 46 different hypotheses were formulated to address determinants, ranging from 6 to 9 hypotheses per determinant. For each determinant, we provide an overview of the most feasible and potentially effective hypotheses (as evaluated by the experts) that organizations can use to develop a tailored implementation plan for youth care guidelines.

Implementation determinants

The implementation of youth care guidelines is a multifaceted process necessitating a systematic approach. It encompasses the dissemination, adoption, and sustained utilization of guidelines. As such, CPs should be aware of the existence and content of these guidelines, be motivated to apply them in practice, and continue their usage [ 64 ]. According to the experts in this study, determinants focusing on engagement (i.e. promoting guideline use, providing mandatory education, the presence of an implementation leader, and management support) were considered relevant to facilitate the implementation process. Engaging appropriate individuals to stimulate the implementation and use of guidelines and interventions is often overlooked during the implementation process [ 65 ]. However, involving all stakeholders, such as management, implementation leaders, and users, in the implementation of guidelines contributes to successful implementation [ 66 ]. Individuals who are more committed to their tasks and supported in their efforts contribute positively to the implementation process.

Mandatory education is categorised in the CFIR construct ‘Process—Engaging’. In this context, mandatory education serves to attract and involve professionals in the use of guidelines. Different theories exist regarding the effects of mandatory education. Some argue that trainees attending voluntary education programs display higher autonomous motivation, translating to genuine interest and personal commitment to guideline implementation [ 67 , 68 ]. Contrary, others have shown increased motivation with mandatory education, suggesting that when education is mandatory, it must be important [ 69 ]. The enforced nature of the education might convey the significance of adherence to these guidelines, leading to increased motivation to implement them effectively.

The presence of a motivated implementation leader, identified as an important facilitator, aligns with findings from studies, particularly those focused on child abuse guidelines [ 33 , 34 , 36 , 70 , 71 ]. Implementation leaders play a pivotal role in facilitating successful guideline implementation by improving networks and communication, enabling access to experts, and lowering the threshold for professionals to seek assistance [ 70 ]. Their presence is also associated with professionals’ improved readiness to care for children and guideline implementation [ 72 ]. Furthermore, poor management support was considered a relevant barrier, which is in line with previous studies [ 34 , 39 ].

Consistent with prior research, knowledge about the use of the guideline was considered as a facilitator [ 33 , 34 , 73 ] while the lack of it was considered a barrier [ 37 , 38 , 43 ]. According to the behaviour framework by Cabana [ 74 ], increasing knowledge is expected to enhance positive attitudes toward the guidelines, ultimately contributing to the effective implementation of guidelines.

The experts considered poor communication skills to be a relevant barrier, aligning with findings from previous studies [ 35 , 37 , 40 , 71 , 75 ]. Effective communication skills are crucial in detecting psychosocial problems, as professionals' abilities and interviewing techniques are linked to parents' disclosure. However, professionals' communication skills often pose a significant challenge when discussing sensitive issues with patients or parents [ 76 , 77 ].

Professionals’ perceived responsibility towards screening for psychosocial problems and their belief that using guidelines will result in positive outcomes were also considered important but more challenging to change in practice compared to factors like knowledge and the availability of resources. Attitudes and beliefs are shaped by past and present experiences [ 78 ] and once established, they are hard to change [ 79 ]. Crapazano [ 79 ] demonstrated that despite an increase in knowledge about alcohol and drug use, professionals' attitudes and beliefs about screening practices and interventions remained negative.

Consistent with the expert consensus study by Huijg [ 44 ], time availability is deemed important but challenging to alter in practice. Activities like screening for psychosocial problems, interprofessional collaboration, and family care are time-consuming. Despite the well-known time constraint in youth care, professionals require support from management and policymakers. Organizations can enhance time management through prioritization workshops, technology integration, purposeful scheduling, and team collaboration platforms. These strategies might empower professionals to navigate time constraints, enhancing overall productivity in the dynamic field of youth care [ 80 , 81 ].

Contrary to previous literature

While the experts in the current study considered guideline promotion and mandatory education relevant for optimal implementation, they are rarely considered facilitators in other studies. This discrepancy could be explained by the fact that researchers often utilize frameworks [ 24 , 33 , 34 , 35 , 40 , 71 , 75 ] or questionnaires [ 39 , 82 ] with preformulated determinants that may not specifically cover these determinants. Additionally, determinants are often identified from the perspective of professionals using the guidelines rather than those facilitating guideline implementation [ 34 ]. Consequently, determinants within the domain of the individual are more likely to be cited than determinants within the domain of the process, s the latter is more focused on the organizational level [ 30 ].

Contrary to our expectations, professionals' fear of false identification after screening for psychosocial problems was considered one of the least important determinants by the experts. This could be attributed to the fact that fear is a frequently cited barrier among mandated reporters of child abuse [ 73 , 83 , 84 , 85 ]. However, in our study, questions were directed towards determinants of the implementation of youth care guidelines focusing on psychological, behavioural, and social problems in children and their families in general. These guidelines do not mandate professionals to report to authorities when they have doubts regarding a child’s development. In many countries, however, professionals are obligated by law to report any reasonable suspicion of child abuse. In child abuse, fear of false identification is therefore perceived as a major barrier.

Implementation hypotheses

To elicit change in engagement-related determinants, the BCTs practical support and action planning were considered the most feasible and potentially effective. There is growing interest in the use of action planning to bridge the gap between behavioural intentions and actual change. The development of an action plan can help initiate change by specifying when, where, and how to act [ 86 , 87 ]. Effective action planning has the potential to enhacnce a positive workplace culture in which both management and professionals are actively engaged and take responsibility for guideline implementation and quality improvement [ 86 , 88 ]. Practical, task–oriented support includes clarifying roles, providing resources to perform tasks, and monitoring implementation [ 89 ]. In Connell’s consensus study, 81% of the experts considered providing practical support to be linked to social influences – interpersonal processes that can cause individuals to change their thoughts, feelings, or behaviours [ 48 ].

Strategies concentrating on the development of stakeholder interrelationships, such as obtaining formal commitments, were regarded as the most feasible and potentially effective in inducing changes in engagement-related determinants. Obtaining formal commitments is recognized as an effective strategy, as indicated by Waltz's consensus study, to enhance the commitment, involvement, and accountability of managers and implementation leaders in the implementation process [ 46 ].

To elicit change in professionals’ knowledge and skills, the experts deemed providing instructions on guideline use and engaging in the practice and rehearsal of the behaviour as the most feasible and potentially effective BCTs. These findings align with prior research investigating the connections between determinants of change and BCTs [ 48 , 60 ].

Various strategies focusing on the training and education of stakeholders, including educational meetings, collaborative learning, and ongoing training, were considered feasible and potentially effective in facilitating change in practice, which is in line with previous research [ 46 , 90 , 91 ]. For example, collaborative learning, an educational approach involving groups of professionals working together to solve problems or create solutions, has shown positive outcomes in healthcare settings. A study on collaborative learning within youth-friendly health services demonstrated improvements in professionals' healthcare knowledge, use of evidence-based resources, empowerment to provide high-quality youth-friendly care, teamwork, and cooperation [ 90 ]. Ongoing training strategies (e.g. booster sessions and follow–up training) appear promising in maintaining acquired knowledge and skills [ 46 , 92 ]. Concerning educational meetings, despite its significant potential in achieving success in knowledge translation and enhancing professionals' practice [ 93 ], a Cochrane review showed only small to moderate effects [ 94 ]. The review emphasizes that the effectiveness of these meetings is influenced by several factors. They prove most effective when utilizing a mixed interactive and didactic format; however, addressing highly complex behaviours may pose a challenge. The perceived seriousness of the targeted outcome affects effectiveness, with smaller impacts observed for outcomes seen as having less serious consequences for patients. Additionally, factors such as attendance rate, intensity, location, and initial compliance also play crucial roles in determining their effectiveness.

Strengths and limitations

One of the strengths of this study is the use of a widely used theoretical method to guide the Delphi study in the formulation of implementation hypotheses. Additionally, we employed various theoretical frameworks to categorize implementation determinants and compile a set of effective and feasible BCTs and strategies. Assessing determinants, BCTs, and strategies with the support of theoretical frameworks helps ensure a theoretically informed approach rather than relying solely on pragmatic considerations. It is anticipated that applying systematic theory-based methods and frameworks will contribute to the long-term effectiveness of the implementation process [ 31 ].

Another strength of the study is that in addition to strategies, we used BCTs to hypothesise how determinants can be best addressed. Implementation research often provides details on the type of strategies to address determinants but fails to describe which techniques are applied to initiate behaviour change. The lack of theoretical rationale and detailed information on behaviour change processes not only limits the design and replication of implementation efforts but also makes it challenging to evaluate what actually contributes to their effectiveness [ 59 , 95 ].

Several limitations should be acknowledged in this study. Firstly, the absence of professionals and policymakers among the experts may introduce bias in the results, as their perspectives on the significance of implementation determinants and effective strategies could differ from those of researchers and experts. [ 96 ].

Additionally, it's important to note that this Delphi study follows a modified version of the classical approach, which starts with closed-ended questions rather than open-ended ones. While this modification aims to provide a solid foundation by offering a set of determinants based on previous empirical research and reducing the workload for experts, it may lead to the omission of some crucial determinants, BCTs, and implementation strategies not included in the preselected list.

This study offers valuable insights into youth care guideline implementation by systematically formulating hypotheses based on expert input. In contrast to studies primarily focusing on determinant-targeted strategies, we delve into specific techniques crucial for behavioural change. By integrating scientific literature with implementation experts' perspectives, our research provides a nuanced understanding of the complex processes vital for successful youth care guideline implementation. Experts identified determinants most relevant by experts for the implementation of youth care guidelines, encompassing engagement, knowledge, and skills. We presented an overview of corresponding implementation hypotheses to guide organizations, policymakers, and professionals in improving the implementation process and outcomes in youth care guidelines. Future research should move beyond superficial effectiveness assessments and delve into the intricacies of how and why implementation strategies lead to positive outcomes. This shift will contribute to a nuanced understanding of the complex dynamics in youth care guideline implementation. Evaluating techniques and processes provides valuable information to develop context-specific interventions, thereby strengthening the overall knowledge base in implementation science and the use of BCTs in healthcare. Additionally, involving stakeholders at all organizational levels during determinant identification and hypothesis formulation is crucial, recognizing that implementation is a multilevel process where each individual can contribute uniquely to improvement.

Availability of data and materials

All data supporting the conclusions of this study are included in the paper and its additional files. Other supporting data are available at: https://doi.org/10.17026/dans-293-q3yx .

Abbreviations

Expert recommendations for implementing change

Behavioural change technique

Theory–informed behaviour change

Consolidated framework for implementation research

UNICEF. Convention on the rights of the child. 1989.

Statistics Netherlands. The Annual Report Youth Monitor 2020 [Jaarrapport 2020 Landelijke Jeugdmonitor] The Hague: Ministry of Health, Welfare and Sport; 2020.

Statistics Netherlands. The Annual Report Youth Monitor 2023 [Jaarrapport 2023 Landelijke Jeugdmonitor] The Hague: Ministry of Health, Welfare and Sport; 2023.

Tausendfreund T, Knot-Dickscheit J, Schulze GC, Knorth EJ, Grietens H. Families in multi-problem situations: backgrounds, characteristics, and care services. Child Youth Serv. 2016;37(1):4–22.

Article   Google Scholar  

Netherlands Youth Institute. Dossier on multi-problem families [Dossier multiprobleemgezinnen] 2014.

Green H, McGinnity Á, Meltzer H, Ford T, Goodman R. Mental health of children and young people in Great Britain, 2004. Basingstoke, Hampshire; 2005.

Moffitt TE, Caspi A. Childhood predictors differentiate life-course persistent and adolescence-limited antisocial pathways among males and females. Dev Psychopathal. 2001;13(2):355–75.

Article   CAS   Google Scholar  

Reef J, Diamantopoulou S, Van Meurs I, Verhulst F, Van Der Ende J. Child to adult continuities of psychopathology: a 24-year follow-up. Acta Psychiatr Scand. 2009;120(3):230–8.

Article   CAS   PubMed   Google Scholar  

Moore SE, Norman RE, Suetani S, Thomas HJ, Sly PD, Scott JG. Consequences of bullying victimization in childhood and adolescence: a systematic review and meta-analysis. World J Psychiatry. 2017;7(1):60.

Article   PubMed   PubMed Central   Google Scholar  

Hofstra MB, Van Der Ende J, Verhulst FC. Child and adolescent problems predict DSM-IV disorders in adulthood: a 14-year follow-up of a Dutch epidemiological sample. JAACAP. 2002;41(2):182–9.

Google Scholar  

Mesman J, Koot HM. Early preschool predictors of preadolescent internalizing and externalizing DSM-IV diagnoses. JAACAP. 2001;40(9):1029–36.

CAS   Google Scholar  

Denholm R, Power C, Thomas C, Li L. Child maltreatment and household dysfunction in a British birth cohort. Child Abus Rev. 2013;22(5):340–53.

Muriel AC, Hwang VS, Kornblith A, Greer J, Greenberg DB, Temel J, et al. Management of psychosocial distress by oncologists. Psychiatr Serv. 2009;60(8):1132–4.

De Graaf I, Speetjens P, Smit F, de Wolff M, Tavecchio L. Effectiveness of the Triple P Positive Parenting Program on behavioral problems in children: a meta-analysis. Behav Modif. 2008;32(5):714–35.

Article   PubMed   Google Scholar  

van Lier P. Preventing disruptive behavior in early elementary schoolchildren: impact of a universal classroom-based preventive intervention. Erasmus University Rotterdam; 2002.

Vliek L, Overbeek G, Orobio de Castro B. Effects of Topper Training on psychosocial problems, self-esteem, and peer victimisation in Dutch children: a randomised trial. PLoS One. 2019;14(11):e0225504.

Scott S, Knapp M, Henderson J, Maughan B. Financial cost of social exclusion: follow up study of antisocial children into adulthood. BMJ. 2001;323(7306):191.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Romeo R, Knapp M, Scott S. Economic cost of severe antisocial behaviour in children-and who pays it. BJPsych. 2006;188(6):547–53.

PubMed   Google Scholar  

Dutch Central Government. Assessment framework reporting code domestic violence and child abuse [Het afwegingskader in de meldcode huiselijk geweld en kindermishandeling]. Ministry of Health, Welfare and Sport & Ministry of Justice and Security; 2018.

Dutch Centre of Youth Health Care. Youth health care guideline for psychosocial problems [JGZ-richtlijn psychosociale problemen]. 2016.

Diderich HM, Fekkes M, Verkerk PH, Pannebakker FD, Velderman MK, Sorensen PJ, et al. A new protocol for screening adults presenting with their own medical problems at the Emergency Department to identify children at high risk for maltreatment. Child Abuse Negl. 2013;37(12):1122–31.

Grol R. Successes and failures in the implementation of evidence-based guidelines for clinical practice. Med Care. 2001:II46–54.

Glasziou P, Haynes B. The paths from research to improved health outcomes. BMJ EBM. 2005;10(1):4–7.

Konijnendijk AA, Boere-Boonekamp MM, Fleuren MA, Haasnoot ME, Need A. What factors increase Dutch child health care professionals’ adherence to a national guideline on preventing child abuse and neglect? Child Abuse Negl. 2016;53:118–27.

Lia-Hoagberg B, Schaffer M, Strohschein S. Public health nursing practice guidelines: an evalution of dissemination and use. PHN. 1999;16(6):397–404.

Gagliardi AR, Alhabib S. Trends in guideline implementation: a scoping systematic review. Implement Sci. 2015;10(1):1–11.

Netherlands Youth Institute. Reform of the Dutch system for child and youth care: 4 years later. 2019.

Jeugdautoriteit. Stand van de Jeugdzorg. The Hague; 2023.

Cane J, O’Connor D, Michie S. Validation of the theoretical domains framework for use in behaviour change and implementation research. Implement Sci. 2012;7(1):37.

Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci. 2009;4(1):1–15.

Nilsen P. Making sense of implementation theories, models, and frameworks. Implementation Science 30 Springer; 2020. p. 53–79.

Powell BJ, Waltz TJ, Chinman MJ, Damschroder LJ, Smith JL, Matthieu MM, et al. A refined compilation of implementation strategies: results from the Expert Recommendations for Implementing Change (ERIC) project. Implement Sci. 2015;10(1):1–14.

Konijnendijk AA, Boere-Boonekamp MM, Kaya AH, Haasnoot ME, Need A. In-house consultation to support professionals’ responses to child abuse and neglect: determinants of professionals’ use and the association with guideline adherence. Child Abus Negl. 2017;69:242–51.

Konijnendijk AA, Boere-Boonekamp MM, Haasnoot-Smallegange RM, Need A. A qualitative exploration of factors that facilitate and impede adherence to child abuse prevention guidelines in Dutch preventive child health care. J Eval Clin Pract. 2014;20(4):417–24.

Schols MW, De Ruiter C, Öry FG. How do public child healthcare professionals and primary school teachers identify and handle child abuse cases? A qualitative study. BMC Public Health. 2013;13(1):1–16.

Diderich HM, Dechesne M, Fekkes M, Verkerk PH, Pannebakker FD, Velderman MK, et al. Facilitators and barriers to the successful implementation of a protocol to detect child abuse based on parental characteristics. Child Abuse Negl. 2014;38(11):1822–31.

Henriksen L, Garnweidner-Holme L, Thorsteinsen KK, Lukasse M. ‘It is a difficult topic’–a qualitative study of midwives experiences with routine antenatal enquiry for intimate partner violence. BMC Pregnancy Childbirth. 2017;17(1):1–9.

Gómez-Fernández MA, Goberna-Tricas J, Payà-Sánchez M. The experiential expertise of primary care midwives in the detection of gender violence during pregnancy. Qual Study Enferm Clin (English Edition). 2019;29(6):344–51.

Rideout L. Nurses’ perceptions of barriers and facilitators affecting the Shaken Baby Syndrome Education Initiative: an exploratory study of a Massachusetts public policy. J Trauma Nurs. 2016;23(3):125–37.

Schalkwijk AA, Nijpels G, Bot SD, Elders PJ. Health care providers’ perceived barriers to and need for the implementation of a national integrated health care standard on childhood obesity in the Netherlands–a mixed methods approach. BMC Health Serv Res. 2016;16(1):1–10.

Lynne EG, Gifford EJ, Evans KE, Rosch JB. Barriers to reporting child maltreatment: do emergency medical services professionals fully understand their role as mandatory reporters? NC Med. 2015;76(1):13–8.

Clarke L, Chana P, Nazzal H, Barry S. Experience of and barriers to reporting child safeguarding concerns among general dental practitioners across Greater Manchester. Br Den J. 2019;227(5):387–91.

Taylor P, Zaichkin J, Pilkey D, Leconte J, Johnson BK, Peterson AC. Prenatal screening for substance use and violence: findings from physician focus groups. Matern Child Health J. 2007;11(3):241.

Huijg JM, Crone MR, Verheijden MW, van der Zouwe N, Middelkoop BJ, Gebhardt WA. Factors influencing the adoption, implementation, and continuation of physical activity interventions in primary health care: a Delphi study. BMC Fam Pract. 2013;14(1):1–9.

Eldredge LKB, Markham CM, Ruiter RA, Fernández ME, Kok G, Parcel GS. Planning health promotion programs: an intervention mapping approach. Hoboken: John Wiley & Sons; 2016. p. 704.

Waltz TJ, Powell BJ, Fernández ME, Abadie B, Damschroder LJ. Choosing implementation strategies to address contextual barriers: diversity in recommendations and future directions. Implement Sci. 2019;14(1):1–15.

Michie S, Johnston M, Abraham C, Lawton R, Parker D, Walker A. Making psychological theory useful for implementing evidence based practice: a consensus approach. BMJ Qual Saf. 2005;14(1):26–33.

Presseau J, Ivers NM, Newham JJ, Knittle K, Danko KJ, Grimshaw JM. Using a behaviour change techniques taxonomy to identify active ingredients within trials of implementation interventions for diabetes care. Implement Sci. 2015;10(1):1–10.

Dalkey N. An experimental study of group opinion: the Delphi method. Futures. 1969;1(5):408–26.

Linstone HA, Turoff M. The Delphi method: techniques and applications. Massachusetts: Addison-Wesley Reading, MA; 1975. 620.

French SD, Green SE, O’Connor DA, McKenzie JE, Francis JJ, Michie S, et al. Developing theory-informed behaviour change interventions to implement evidence into practice: a systematic approach using the Theoretical Domains Framework. Implement Sci. 2012;7(1):1–8.

Qualtrics. Qualtrics [software]. 2020. ed. Provo, Utah. https://www.qualtrics.com .

Jünger S, Payne SA, Brine J, Radbruch L, Brearley SG. Guidance on Conducting and REporting DElphi Studies (CREDES) in palliative care: Recommendations based on a methodological systematic review. Palliat Med. 2017;31(8):684–706.

Central Committee on Research Involving Humans. Medical Research Involving Human Subjects Act 2022. https://wetten.overheid.nl/BWBR0009408/2022-07-01 . Accessed 10 Okt 2022.

Trevelyan EG, Robinson N. Delphi methodology in health research: how to do it? Eur J Integr Med. 2015;7(4):423–8.

Dubbeldeman EM, van der Kleij RMJJ, Sprenger M, Aslam AS, Crone MR. Determinants influencing the implementation of domestic violence and child abuse and neglect guidelines: a systematic review [Manuscript in progress].

Groome D, Eysenck M. An introduction to applied cognitive psychology. Psychology Press; 2016. 410. https://doi.org/10.4324/9781315732954

van Stralen MM, Lechner L, Mudde AN, de Vries H, Bolman C. Determinants of awareness, initiation and maintenance of physical activity among the over-fifties: a Delphi study. Health Educ Res. 2010;25(2):233–47.

Connell LE, Carey RN, De Bruin M, Rothman AJ, Johnston M, Kelly MP, et al. Links between behavior change techniques and mechanisms of action: an expert consensus study. Ann Behav Med. 2019;53(8):708–20.

Carey RN, Connell LE, Johnston M, Rothman AJ, De Bruin M, Kelly MP, et al. Behavior change techniques and their mechanisms of action: a synthesis of links described in published intervention literature. Ann Behav Med. 2019;53(8):693–707.

Waltz TJ, Powell BJ, Matthieu MM, Damschroder LJ, Chinman MJ, Smith JL, et al. Use of concept mapping to characterize relationships among implementation strategies and assess their feasibility and importance: results from the Expert Recommendations for Implementing Change (ERIC) study. Implement Sci. 2015;10(1):1–8.

Schmidt RC. Managing Delphi surveys using nonparametric statistical techniques. Decis Sci. 1997;28(3):763–74.

Hirschhorn F, Veeneman W, van de Velde D. Inventory and rating of performance indicators and organisational features in metropolitan public transport: a worldwide Delphi survey. Res Transp Econ. 2018;69:144–56.

Fleuren M, Stals K, Ooms H, Weeda C. Youth health care guidelines: a basis for national implementation [Richtlijnen in de jeugdgezondheidszorg: onderbouwing voor landelijke invoering]. 2014.

Pronovost PJ, Berenholtz SM, Needham DM. Translating evidence into practice: a model for large scale knowledge translation. BMJ. 2008. https://doi.org/10.1136/bmj.a1714 .

Greenhalgh T, Robert G, Macfarlane F, Bate P, Kyriakidou O. Diffusion of innovations in service organizations: systematic review and recommendations. Milbank Q. 2004;82(4):581–629.

Curado C, Henriques PL, Ribeiro S. Voluntary or mandatory enrollment in training and the motivation to transfer training. Int J Train Dev. 2015;19(2):98–109.

Baldwin TT, Magjuka RJ, Loher BT. The perils of participation: effects of choice of training on trainee motivation and learning. Pers Psychol. 1991;44(1):51–65.

Tsai WC, Tai WT. Perceived importance as a mediator of the relationship between training assignment and training motivation. Pers Rev. 2003;32(2):151–63.

Tiyyagura G, Schaeffer P, Gawel M, Leventhal JM, Auerbach M, Asnes AG. A qualitative study examining stakeholder perspectives of a local child abuse program in community emergency departments. Acad Pediatr. 2019;19(4):438–45.

Louwers EC, Korfage IJ, Affourtit MJ, De Koning HJ, Moll HA. Facilitators and barriers to screening for child abuse in the emergency department. BMC Pediatr. 2012;12(1):1–6.

Gausche-Hill M, Ely M, Schmuhl P, Telford R, Remick KE, Edgerton EA, et al. A national assessment of pediatric readiness of emergency departments. JAMA Pediatr. 2015;169(6):527–34.

Tiyyagura G, Gawel M, Koziel JR, Asnes A, Bechtel K. Barriers and facilitators to detecting child abuse and neglect in general emergency departments. Ann Emerg Med. 2015;66(5):447–54.

Cabana MD, Rand CS, Powe NR, Wu AW, Wilson MH, Abboud PAC, et al. Why don’t physicians follow clinical practice guidelines? A framework for improvement. JAMA. 1999;282(15):1458–65.

Roelens K, Verstraelen H, Van Egmond K, Temmerman M. A knowledge, attitudes, and practice survey among obstetrician-gynaecologists on intimate partner violence in Flanders. Belgium BMC Public Health. 2006;6(1):1–10.

Wissow LS, Wilson ME, Roter DL. Pediatrician interview style and mothers’ disclosure of psychosocial issues. Pediatrics. 1994;93(2):289–95.

Wissow LS, Larson S, Anderson J, Hadjiisky E. Pediatric residents’ responses that discourage discussion of psychosocial problems in primary care. Pediatrics. 2005;115(6):1569–78.

Allport GW. Attitudes: a handbook of social psychology. Worcester: Clark University Press; 1935.

Crapanzano K, Vath RJ, Fisher D. Reducing stigma towards substance users through an educational intervention: harder than it looks. Acad Psychiatry. 2014;38(4):420–5.

Pitre C, Pettit K, Ladd L, Chisholm C, Welch JL. Physician time management. MedEdPORTAL. 2018;14:10681.

Kleshinski O, Dunn TG, Kleshinski JF. A preliminary exploration of time management strategies used by physicians in the United States. Int J Med Educ. 2010;1:47–54.

Schweitzer RD, Buckley L, Harnett P, Loxton NJ. Predictors of failure by medical practitioners to report suspected child abuse in Queensland, Australia. Aust Health Rev. 2006;30(3):298–304.

Lee H-M, Kim J-S. Predictors of intention of reporting child abuse among emergency nurses. J Pediatr Nurs. 2018;38:e47-52.

Jones R, Flaherty EG, Binns HJ, Price LL, Slora E, Abney D, et al. Clinicians’ description of factors influencing their reporting of suspected child abuse: report of the Child Abuse Reporting Experience Study Research Group. Pediatrics. 2008;122(2):259–66.

Bjørknes R, Iversen AC, Nordrehaug Åstrøm A, Vaksdal BI. Why are they reluctant to report? A study of the barriers to reporting to child welfare services among public dental healthcare personnel. Health Soc Care Community. 2019;27(4):871–9.

Clutter PC, Reed C, Cornett PA, Parsons ML. Action planning strategies to achieve quality outcomes. Crit Care Nurs Q. 2009;32(4):272–84.

Sniehotta FF, Schwarzer R, Scholz U, Schüz B. Action planning and coping planning for long-term lifestyle change: theory and assessment. Eur J Soc Psychol. 2005;35(4):565–76.

O’Neal H, Manley K. Action planning: making change happen in clinical practice. Nurs Stand. 2007;21(35):35–40.

CAS   PubMed   Google Scholar  

Yukl G. Leadership in organizations, Pearson Education; 2019. 560 p.

Lesco G, Squires F, Babii V, Bordian N, Cernetchi O, Hilber AM, et al. The feasibility and acceptability of collaborative learning in improving health worker performance on adolescent health: findings from implementation research in Moldova. BMC Health Serv Res. 2019;19(1):1–11.

Grimshaw J, Thomas R, MacLennan G, Fraser C, Ramsay C, Vale L, et al. Effectiveness and efficiency of guideline dissemination and implementation strategies. Health Technol Assess. 2004. https://doi.org/10.3310/hta8060 .

Henggeler SW, Sheidow AJ, Cunningham PB, Donohue BC, Ford JD. Promoting the implementation of an evidence-based intervention for adolescent marijuana abuse in community settings: testing the use of intensive quality assurance. J Clin Child Adolesc Psychol. 2008;37(3):682–9.

Medves J, Godfrey C, Turner C, Paterson M, Harrison M, MacKenzie L, et al. Systematic review of practice guideline dissemination and implementation strategies for healthcare teams and team-based practice. Int J Evid Based Healthc. 2010;8(2):79–89.

Forsetlund L, O’Brien MA, Forsen L, Mwai L, Reinar LM, Okwen MP, et al. Continuing education meetings and workshops: effects on professional practice and health care outcomes. Cochrane Database Syst Rev. 2021. https://doi.org/10.1002/14651858.CD003030.pub2 .

Davidson KW, Goldstein M, Kaplan RM, Kaufmann PG, Knatterud GL, Orleans CT, et al. Evidence-based behavioral medicine: what is it and how do we achieve it? Ann Behav Med. 2003;26(3):161–71.

Abidi L, Oenema A, Nilsen P, Anderson P, van de Mheen D. Strategies to overcome barriers to implementation of alcohol screening and brief intervention in general practice: a Delphi study among healthcare professionals and addiction prevention experts. Prev Sci. 2016;17(6):689–99.

World Medical Association. Declaration of Helsinki: Ethical principles for medical research involving human subjects 2013 [cited 2022 Oktober 10]. Available from: https://www.wma.net/policies-post/wma-declaration-of-helsinki-ethical-principles-for-medical-research-involving-human-subjects/ .

General Data Protection Regulation (GDPR). Regulation (EU) 2016/679 2016. https://gdpr-info.eu/ . Accessed 10 Okt 2022.

Download references

Acknowledgements

The authors wish to acknowledge all participants for their time completing the surveys.

This study was funded by ZonMw, the Netherlands Institute for Health Research and Development (grant number 729220001).

Author information

Authors and affiliations.

Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, the Netherlands

Eveline M. Dubbeldeman, Rianne M. J. J. van der Kleij, Evelyn A. Brakema & Mathilde R. Crone

Department of Health Promotion, Maastricht University, Maastricht, the Netherlands

Mathilde R. Crone

You can also search for this author in PubMed   Google Scholar

Contributions

EMD was involved in the design of the study, designed the questionnaire, recruited participants, collected, analysed and interpreted the data, and wrote the initial draft and final manuscript. RMJJ assisted in the design of the study, designed the questionnaire, analysed and interpreted the data, and critically revised the manuscript. EAB assisted in the interpretation of the data and critically revised the manuscript. MRC assisted in the design of the study and interpretation of the data and critically revised the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Eveline M. Dubbeldeman .

Ethics declarations

Ethics approval and consent to participate.

The Medical Ethics Committee of the Leiden University Medical Center, decided that the rules laid down in the Dutch Medical Research Involving Human Subjects Act (in Dutch: ‘Wet Medisch-wetenschappelijk Onderzoek met mensen’) did not apply to the research proposal (proposal number 22-3079). We certify that all methods were in full compliance with the Declarations of Helsinki [ 97 ] and the General Data Protection Regulation [ 98 ]. The questionnaire was sent via an internet link and the data were processed without identifiers. Experts eligible for participation were informed about the study and its procedures before the study commenced, and online informed consent of the participants was obtained at the start of the online survey. Experts had the right to withdraw his or her consent at any time.

Consent for publication

Not applicable.

Competing interests

The authors have no competing interests to declare.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

12961_2024_1167_moesm1_esm.docx.

Additional file 1. Recommendations for the Conducting and REporting of DElphi Studies (CREDES) checklist [ 53 ].

Additional file 2. Overview of participating experts ( n  = 14).

Additional file 3. determinants’ level of importance and changeability as indicated by experts ( n  = 14)., additional file 4. summary of the results of rounds 2 and 3 ( n  = 14)., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Dubbeldeman, E.M., van der Kleij, R.M.J.J., Brakema, E.A. et al. Expert consensus on multilevel implementation hypotheses to promote the uptake of youth care guidelines: a Delphi study. Health Res Policy Sys 22 , 89 (2024). https://doi.org/10.1186/s12961-024-01167-x

Download citation

Received : 10 January 2023

Accepted : 23 June 2024

Published : 02 August 2024

DOI : https://doi.org/10.1186/s12961-024-01167-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Implementation
• Determinants
• Behaviour change technique

Health Research Policy and Systems

ISSN: 1478-4505

• Submission enquiries: Access here and click Contact Us
• General enquiries: [email protected]

• Research article
• Open access
• Published: 07 August 2024

De novo genome assembly of white clover ( Trifolium repens L.) reveals the role of copy number variation in rapid environmental adaptation

• Wen-Hsi Kuo 1 ,
• Sara J. Wright 1 , 2 ,
• Linda L. Small 1 &
• Kenneth M. Olsen   ORCID: orcid.org/0000-0002-8338-3638 1  

BMC Biology volume  22 , Article number:  165 ( 2024 ) Cite this article

Metrics details

White clover ( Trifolium repens ) is a globally important perennial forage legume. This species also serves as an eco-evolutionary model system for studying within-species chemical defense variation; it features a well-studied polymorphism for cyanogenesis (HCN release following tissue damage), with higher frequencies of cyanogenic plants favored in warmer locations worldwide. Using a newly generated haplotype-resolved genome and two other long-read assemblies, we tested the hypothesis that copy number variants (CNVs) at cyanogenesis genes play a role in the ability of white clover to rapidly adapt to local environments. We also examined questions on subgenome evolution in this recently evolved allotetraploid species and on chromosomal rearrangements in the broader IRLC legume clade.

Integration of PacBio HiFi, Omni-C, Illumina, and linkage map data yielded a completely de novo genome assembly for white clover (created without a priori sequence assignment to subgenomes). We find that white clover has undergone extensive transposon diversification since its origin but otherwise shows highly conserved genome organization and composition with its diploid progenitors. Unlike some other clover species, its chromosomal structure is conserved with other IRLC legumes. We further find extensive evidence of CNVs at the major cyanogenesis loci; these contribute to quantitative variation in the cyanogenic phenotype and to local adaptation across wild North American populations.

Conclusions

This work provides a case study documenting the role of CNVs in local adaptation in a plant species, and it highlights the value of pan-genome data for identifying contributions of structural variants to adaptation in nature.

White clover ( Trifolium repens L., 2n = 4x = 32) is the most widely grown temperate forage legume worldwide due to its superior nitrogen-fixing ability, perenniality, and resilience in frequently disturbed habitats [ 1 , 2 , 3 ]. Prior to the twentieth century invention of synthetic nitrogen fertilizer, this species was so important as a source of soil nitrogen that it was considered the agricultural equivalent of coal in fueling agricultural expansion during the Industrial Revolution [ 4 ]. In addition to its longstanding agricultural importance, white clover is also a well-known eco-evolutionary model system for studying how selection maintains within-species chemical defense variation. The species is characterized by a genetically well characterized polymorphism for cyanogenesis (HCN release following tissue damage); multiple studies over the last 80 years have established that climate-associated clines in cyanogenesis evolve rapidly across latitudinal and other environmental gradients worldwide, with cyanogenic plants generally predominating in warmer locations [ 5 , 6 , 7 , 8 ]. While native to Europe, white clover has become widely naturalized in mesic environments worldwide (from subtropical to boreal climates), and recent genomic research has linked its ability to become so widely adapted to its allopolyploid origin from two ecologically distinct diploid progenitor species ( T. occidentale and T. pallescens ) [ 9 ].

White clover’s importance both in agriculture and as an eco-evolutionary model system has spurred recent efforts to develop high-quality genetic and genomic resources for the species [ 9 , 10 , 11 , 12 , 13 ]. Most recently, two independent genome sequencing projects have utilized long-read sequencing technologies to overcome the scaffolding difficulties associated with polyploid genome assembly [ 11 , 12 ]. While these two high-quality genomes are a major step in white clover studies, the recent growth of plant pangenome projects (e.g., [ 14 , 15 , 16 ]) has demonstrated clearly that multiple high-quality reference genomes are required to properly understand the genomic structure and intraspecific diversity of a species; this is especially true when patterns of genomic structural variation contribute to natural phenotypic variation. In addition, high-quality reference genomes can facilitate investigations of genome evolution on a macroevolutionary scale. For white clover specifically, genome sequence comparisons between this species and related legume genera could fill a current knowledge gap concerning karyotype evolution in the inverted repeat-lacking clade (IRLC) of the legume family, which contains multiple economically important crop species, such as soybean and chickpea, and is characterized by extensive genome rearrangements [ 17 ].

Among the many types of structural variation that can occur in a genome, copy number variation (CNV), defined as the variable repetition of specific sequence motifs ranging from 50 bp to several Mbp, is a major contributor to both genetic and phenotypic variability across eukaryotes [ 18 , 19 , 20 , 21 , 22 ]. Despite abundant evidence that CNVs are pervasive in genomes and that they contribute to phenotypic variation, such as in domesticated crops [ 19 , 23 , 24 ], there are remarkably few documented cases where CNVs have been shown to underlie adaptation in wild species [ 25 , 26 , 27 ]. To the best of our knowledge, the only potential example in a plant species involves flowering time regulation in common waterhemp, where CNVs of an ATP synthesis pathway locus appear related to natural flowering time variation [ 25 ]. Consequently, our knowledge about the role of CNVs in shaping local adaptation in wild populations is lacking, particularly for plant species. Even in the model plant Arabidopsis, studies of CNVs have been restricted to either documenting genome-wide CNV distributions without knowledge of associated phenotypes (e.g., [ 16 ]), or to functional characterization of the phenotypic impact of CNVs without data from natural populations to assess their role, if any, in adaptation (e.g., [ 28 ]).

Based on our prior knowledge of white clover and the genetic basis of adaptation in this species, we hypothesized that local environmental adaptation in wild populations could arise, in part, through CNVs, particularly at the loci known to control the well-documented cyanogenesis polymorphism. This chemical defense polymorphism is controlled by two independently segregating simple Mendelian genetic polymorphisms that determine the presence or absence of two cyanogenic chemical precursors, both of which must be present for a plant to produce the cyanogenic phenotype: (1) Ac/ac , controlling the presence/absence of cyanogenic glucosides, and (2) Li/li , controlling the presence/absence of their hydrolyzing cyanogenic β-glucosidase enzyme, linamarase (reviewed by [ 29 ]). At the molecular level, Ac is a 3-gene metabolic cluster on chromosome 2 that comprises the cyanogenic glucoside biosynthetic pathway, while Li is a single gene located on chromosome 12 that encodes linamarase [ 13 , 30 , 31 , 32 ]. Interestingly, the Ac gene is only present in the T. occidentale subgenome, and the Li gene is only present in the T. pallescens subgenome, which suggests that the two genes in white clover were contributed by its two different diploid progenitors [ 13 ]. For both loci, the recessive (nonfunctional) alleles are the result of gene deletions, meaning that both the Ac/ac and Li/li polymorphisms are gene presence/absence variations (PAVs) [ 30 , 32 , 33 ]. However, while the recessive ac and li alleles have consistently been shown to be gene deletions [ 34 , 35 ], our observations of Ac and Li dominant allele inheritance in greenhouse pedigree populations suggested the occurrence of CNVs at both loci (see the “ Results ” section). Given that variability in the cyanogenesis phenotype is known to contribute to white clover’s adaptation across climatic gradients [ 5 , 6 , 29 , 36 ], we therefore hypothesized that CNVs at the Ac and Li loci could play an important role in this adaptive response.

In this study, we tested the hypothesis that CNVs at the Ac and Li cyanogenesis loci contribute to natural adaptive variation in white clover. We used PacBio HiFi, Omni-C, and two linkage maps to generate and de novo assemble a new chromosome-scale and haplotype-resolved genome. We then compared structural variation at the cyanogenesis loci among three high-quality white clover genomes (this study; [ 11 , 12 ]). Next, we assessed Ac and Li CNV occurrence and distributions across wild populations spanning much of the North American species range (419 accessions across 43 locations). In a complementary analysis, we examined the contribution of CNVs to natural phenotypic variation by assessing the relationship between Ac CNVs, gene expression, and cyanogenic glucoside content. We further tested whether CNVs at the cyanogenesis loci contribute to adaptation in nature by conducting association analyses between the CNVs and the local environments of the sampled wild populations. Finally, we leveraged our high-quality genome to address questions concerning karyotype evolution in the clover genus Trifolium and related genera in the “IRLC clade” of the legume family (Fabaceae). To our knowledge, this study is among the first to document the contribution of CNVs to local environmental adaptation in a wild plant species, and it also provides new insights into the history of genome rearrangements in an economically important clade of legume species.

Genome assembly statistics

We isolated DNA from a wild North American white clover accession (GFL_007) to assemble a haplotype-resolved white clover genome using a combination of PacBio HiFi, Omni-C, and linkage map technologies. The total length of the primary haplotig was 995,597,458 bp and contained 1,033 contigs ( N 50  = 17,751,673 bp). After Omni-C and linkage map scaffolding, 94.529% of the haplotig sequences were placed into 16 linkage groups, corresponding to the 16 chromosomes in white clover ( N 50  = 57,173,913 bp). The total length of the alternative haplotig was of similar size (1,002,960,897 bp) and contained 584 contigs ( N 50  = 15,995,289 bp); after scaffolding (performed independently of the primary haplotig), 95.546% of the alternative haplotig sequences were placed into 16 chromosome scale linkage groups ( N 50  = 60,046,767 bp). An independent Illumina-sequenced dataset (2 × 150 bp, 185 million read pairs; SRR27541471) of the same plant accession was used for genome size estimation and quality assessment. The estimated short-read haploid genome size was similar to the PacBio HiFi assembly (1,025,512,829 bp), with 1.46% heterozygous sites (k-mer length = 21) (Additional file 4 : Fig. S1). The consensus quality (QV) of the diploid genome was 51.3697, indicating > 99.99% accuracy in the consensus base calls. The k-mer completeness was 98.4765%.

Approximately 60% of the genome assembly was made up of repeated elements; half of these (30% of the genome) were retroelements, which closely matches retroelement proportions in the two recently published long-read assemblies [ 11 , 12 ]. The retroelements did not show any biased distribution between the two white clover subgenomes. Furthermore, our phylogenetic analyses revealed several major, recent retroelement diversification events in the white clover genome (Additional file 4 : Fig. S2, Fig. S3). These patterns are consistent with transposable element (TE) proliferation following the allopolyploid speciation origin of white clover, a phenomenon that has been reported in other allopolyploid systems [ 37 ]. Unfortunately, due to the lack of high-quality genomes of the two diploid progenitor species ( T. occidentale and T. pallescens ), a direct test of this TE proliferation hypothesis is currently unfeasible.

BUSCO assessments indicated that 98% of the single-copy orthologous genes of the fabales_odb10 database (5366 genes) were covered in our assembly. Of these, 82% were found to be duplicated, which is consistent with the allotetraploid origin of white clover. More detailed genome assembly statistics are provided in Additional file 1 : Table S1. The quality of the separation of the two haplotigs was assessed by comparing the k-mer distributions between the raw reads, the primary haplotig and the alternative haplotig (Fig.  1 A, B). The k-mer multiplicity distributions of the primary and alternative haplotigs show nearly complete overlap (indicated by purple shading in Fig.  1 A), suggesting a highly accurate separation of the genome’s two haplotigs. The k-mer distribution of the diploid genome indicates that most k-mers were found only once (Fig.  1 B), consistent with the high heterozygosity of the genome. The much lower peaks of the 3- or 4-hit distributions compared to 1- or 2-hit distributions indicate that there is divergence between homeologous sequences of the two subgenomes; this suggests that while the genome is tetraploid, it likely behaves meiotically as a diploid species.

Genome quality assessment and subgenome identity characterization. A k-mer multiplicity distribution between the primary and alternative haplotigs. Purple color indicates the overlap between the primary and alternative haplotigs, reflecting their nearly identical distributions. B The number of the times of the k-mers are found in the diploid genome. C Omni-C heatmap visualized by KR normalization method with window size at 1 Mbp. D Linkage map visualized by calculating the LOD (upper triangle) and the recombination fraction (RF, lower triangle) of the genetic markers of the DG F 2 mapping population at their physical location. Only the primary haplotig is presented here. E Whole genome nucleotide alignment of the primary and alternative haplotigs (identity > 95%, query length > 10,000 bp, hit length > 10,000 bp). F Chromosomes categorized into T. occidentale (To) or T. pallescens (Tp) subgenomes. The gradient color is the mapping rate to either diploid progenitor, where − 1 is ideally completely T. pallescens -like and + 1 is ideally completely T. occidentale -like; values exceeding that range indicate mapping to multiple locations in the diploid progenitors’ genomes

Omni-C and linkage map scaffolding and subgenome characterization

The two haplotigs of the contiguous genome were first scaffolded by Omni-C linked reads and then scaffolded by our two previously published linkage maps (DG and GS F 2 mapping populations; 6,173 unique GBS SNP markers after combining the two mapping populations) [ 13 ]. Where there were conflictions between the Omni-C and linkage map scaffolding results, we prioritized the results from the linkage maps (see the “ Discussion ” section). The read count distribution of the chromatin contact map of the scaffolded genome supports our finding that our 16 scaffolds (pseudomolecules) are high-quality representatives of the 16 white clover chromosomes (Fig.  1 C). Although we do see weak signals between the homeologous chromosomes (e.g., between chr 1 and chr 9), we believe this result can be attributed to the highly similar gene sequences (see BUSCO analyses in Additional file 1 : Table S1) and inaccurate read-mapping of Omni-C short-reads (2 × 150 bp). In addition, the inter-homeologous signals are not evident in the recombination map (Fig.  1 D), as most of the GBS markers used in linkage map construction are in the non-genic regions, which are more divergent between the homeologous chromosomes.

The subgenome identities of the 16 scaffolds/chromosomes were assessed using BLAST against the two diploid progenitors’ genomes. Across the board, there were more hits to the T. occidentale genome than to the T. pallescens genome (Fig.  1 F). This is consistent with previous evidence that present-day individuals of T. occidentale are genomically highly similar to the ancestral species that contributed one of white clover’s subgenomes (chr 1–chr 8), whereas present-day representatives of T. pallescens are somewhat diverged from the ancestral species that contributed the other subgenome (chr 9–16) [ 38 ]. When setting the mapping threshold at 0.45 (where − 1 is completely T. pallescens -like and + 1 is completely T. occidentale -like; see the “ Methods ” section), we were able to categorize the 16 chromosomes into eight of T. occidentale origin (chr 1–chr 8) and eight of T. pallescens origin (chr 9–chr 16) (Fig.  1 F and Additional file 4 : Fig. S4). We detected no obvious inter-chromosomal translocation signal, except for at the center of the chromosomes where the signals were ambiguous; this is likely because these regions include centromeric long tandem repeats, which can interfere with the mapping process. However, we cannot validate this conclusion due to the limited resolution of the diploid progenitors’ genomes [ 9 ].

As predicted, our assessments of haplotig similarity using our whole genome nucleotide alignment revealed clear alignment hits between the primary and alternative haplotigs (indicated by the major blue diagonal line in Fig.  1 E). In contrast, hits between homeologous loci are much less evident (the two weak blue diagonal lines in the top-left and bottom-right quadrants of Fig.  1 E). We also found that there are generally more intra-subgenome hits than inter-subgenome hits (Fig.  1 E and Additional file 1 : Table S2). Together, these observations suggest that inter-subgenome recombination (i.e., homeologous translocation) was very limited after the polyploidization event that created white clover and that the integrity of the two parental species’ subgenomes has been preserved, in agreement with the same conclusion inferred from the k-mer analysis above and previous analysis [ 9 ].

Gene synteny and synonymous mutation rate

We annotated 96,293 protein coding genes in the primary haplotig, which is slightly more than double the number of genes in the diploid genome of the related legume Medicago truncatula (44,295); this finding is consistent with white clover’s tetraploid genome. A total of 61,352 of the annotated genes were successfully mapped by BLAST against the NCBI RefSeq database and functionally annotated with the GO database. The median lengths of the concatenated coding sequence (CDS) and intron sequence per gene were 927 bp and 1693 bp, respectively, which is comparable to closely related legume species (Additional file 4 : Fig. S5). The gene synteny between the two subgenomes was especially high (Fig.  2 A), suggesting the lack of any large-scale genome rearrangements in white clover or its closest relatives either before or after the polyploidization event that produced the species. The orthologous synonymous rate ( K S ) analysis showed a neutral genetic distance to the other species, reflecting the species-level phylogenetic relationships (Fig.  2 B). Interestingly, the K A / K S distribution for the two white clover subgenomes [ T. repens (occ) vs. T. repens (pall)] was statistically indistinguishable from the K A / K S of the two diploid progenitor species [ T. pallescens vs. T. occidentale ] (Additional file 4 : Fig. S6). This pattern suggests that, following the polyploidization event that created white clover, the homeologous gene copies within the tetraploid genome have exhibited no detectable sign of relaxed purifying selection, despite the high likelihood of functional redundancy among the many gene duplicates.

Genome assembly of white clover. A Circular view of genomic feature distribution along the 16 chromosomes. The blue, green, and brown tracks stand for the GC content, gene density, and repetitive element density, respectively. The links show the gene synteny between the two subgenomes, where chromosomes 1–8 (light green) and chromosomes 9–16 (light blue) belong to the T. occidentale (To) and T. pallescens (Tp) subgenomes, respectively. B Pairwise synonymous mutation rate (K S ) between the subgenomes and closely related species in the legume family (Fabaceae, subfamily Papilionoideae). C Gene synteny plot and consensus gene tree of the inverted repeat-lacking clade (IRLC) within subfamily Papilionoideae. The node numbers show the crown ages in Myr, which are adapted from [ 39 ]. For visualization purposes, only the synteny of the chromosome 1–8 was plotted. The plot of the chromosome 9–16 is highly similar

Interspecific gene synteny analysis indicated that the white clover genome is highly syntenic to the closely related species Medicago truncatula (24.53 MYr divergence) and moderately syntenic to the more distantly related Cicer arietinum (32.01 MYr divergence) (Fig.  2 C). There were only two chromosome-scale translocations between M. truncatula and white clover: between Mt chr 2 and Tr chr 6 and between Mt chr 4 and Tr chr 8 (Fig.  2 C and Additional file 4 : Fig. S7E, F). Given the relatively high synteny between these species in different genera, we were surprised to detect multiple genome rearrangements within the genus Trifolium when comparing white clover with its two congener species with reference genomes, T. subterraneum and T. pratense (12.97 MYr divergence). Trifolium subterraneum has the same basal chromosome number as white clover ( n  = 8); however, this lineage appears to have undergone at least 2 or 3 chromosome-scale translocations in chr 2, chr 3, chr 4, chr 6, chr7, and chr 8 (Fig.  2 C and Additional file 4 : Fig. S7A, B). Trifolium pratense , in comparison, appears to have lost one chromosome after divergence from the T. subterraneum lineage, and it shows more complex patterns of genomic rearrangements (Fig.  2 C and Additional file 4 : Fig. S7C, D). Based on the phylogeny and the gene synteny analyses, we therefore infer that among the three Trifolium species with reference genomes, white clover preserves the more ancestral genome structure, and that there have been extensive rearrangements among its diploid congeners.

Cyanogenesis genes and copy number variation

Genome sequence analyses.

We hypothesized that CNVs at the Ac and Li loci contribute to the phenotypic variation found in white clover populations across environmental gradients. This hypothesis was initially based on patterns of SNP inheritance in greenhouse pedigree populations, where Sanger sequencing of PCR amplicons revealed that Acac and Lili hemizygotes could transmit dominant alleles with SNPs that appeared heterozygous (Additional file 2 : Table S3). To assess this hypothesis with our whole-genome data, we first tested for evidence of gene copy number variation (CNV) between the primary and alternate haplotigs in our newly generated genome. We found that our long-read based assembly unambiguously confirmed the presence of CNVs at both loci. We aligned the sequences around the Ac gene cluster (comprising the three cyanogenic glucoside biosynthesis genes: CYP79D15 , CYP736A187 , and UGT85K17 ) and the Li gene and performed BLAST analyses between haplotigs (Fig.  3 ). For Ac , there are two complete gene clusters in the primary haplotig and three in the alternative haplotig (Fig.  3 A; Additional file 1 : Table S4). The gene order inside the Ac gene cluster is consistent with previous findings [ 32 ] (Additional file 4 : Fig. S8). For Li , there are two gene copies in both haplotigs (Fig.  3 B; Additional file 1 : Table S4). To determine the span of the Ac and Li CNV repeat motif, we plotted the gene synteny in the chromosomal region surrounding each locus (Fig.  3 C, D). A single copy of the Ac motif is approximately 255–320 Kbp (Additional file 1 : Table S5), while a single copy of the Li motif is approximately 24 Kbp (Additional file 1 : Table S6). Although the CNV motifs are clearly replicated, the non-coding regions between the copies show sequence divergence (Fig.  3 C, D). Multiple repetitive sequences are evident in the Li gene region, including other glucosidase genes that are predicted to have different substrate specificities than linamarase [ 40 ] (Fig.  3 D and Additional file 4 : Fig. S9). The presence of these repetitive sequences could contribute to genome instability that underlies the structural variation of this genomic region.

Nucleotide alignment and gene synteny of the white clover cyanogenesis genes. A , B Nucleotide alignment of the regions around the Ac gene cluster and the Li locus. Blue, the pairwise alignment is in the same direction. Red, the pairwise alignment is in the opposite direction. C , D Gene synteny of the regions around the Ac gene cluster ( C ) and the Li locus ( D ). Schematic plot on the top-right of each panel shows the overall arrangement of the repeated gene copies that are shown in detail in the main plots. Top sequence, the chromosome in the primary haplotig. Bottom sequence, the corresponding homeologous chromosome in the alternative haplotig. Gray links, nucleotide alignments (same as in A and B ). Purple stacks, the number of nucleotide alignment hits in the homeologous chromosome. Note that Chr. 2 is in the T. occidentale subgenome and Chr. 12 is in the T. pallescens subgenome

In addition to comparing CNVs in our newly generated genome, we performed similar analyses with the two other published white clover genomes. In the haplotig-resolved genome of Santangelo et al. [ 12 ], we found two copies of the Ac gene cluster in the primary haplotig and a deletion of the entire Ac gene cluster in the alternative haplotig (a ~ 500 Kbp deletion compared to the primary haplotig in the present study) (Additional file 4 : Fig. S10A, B). For the Li gene, we found two copies per haplotig, similar to our new genome assembly (Additional file 4 : Fig. S10C, D). In the genome of Wang et al. [ 11 ], which does not provide haplotig resolution, we found three copies of the Ac gene cluster and two copies of the Li gene (Additional file 4 : Fig. S11). In none of the three white clover whole genomes did we find a haplotig with a complete deletion of the Li locus. However, since Li deletions are known to be common in nature [ 30 , 35 ], we wanted to estimate of the size of the Li genomic deletion in plants where it occurs. To do this we mapped the raw reads (2 × 150 bp) of the genome sequences of an accession lacking linamarase activity (DMN_010, lili genotype, NCBI SRR27541472) to the primary haplotig of our novel genome. In the lili accession, we found a low coverage window of ~ 500 Kbp (Additional file 4 : Fig. S12); this result suggests a much larger genomic deletion than the span of the Li CNV motif (24 Kbp) alone.

Gene expression and phenotypic data

After demonstrating the existence of both Ac and Li CNVs in all three white clover whole genomes, we sought to test the hypothesis that CNVs at the Ac and Li loci directly affect the cyanogenesis phenotype. To do this, we focused on the Ac locus because synthesis of cyanogenic glucosides, mediated by the Ac gene cluster, is predicted to be directly correlated with the cyanogenic response [ 41 ]. We performed qPCR of genomic DNA and mRNA to estimate the correlation between CNV counts and expression in the Ac gene cluster ( CYP79D15 , CYP736A187 , UGT85K17 ). First, we found that the inferred copy numbers of the three genes in the Ac cluster consistently occur in a 1:1:1 ratio (Additional file 4 : Fig. S13); this is consistent with our findings above that the entire Ac gene cluster is duplicated as a unit. In addition, we found that cyanogenic glucoside content is significantly positively correlated with copy number for each of the three genes in the Ac cluster (Fig.  4 A–C and Additional file 1 : Table S7). In comparison, no such correlation was observed for the Li gene, whose function is unrelated to cyanogenic glucoside biosynthesis (Fig.  4 D and Additional file 1 : Table S7). We further detected that cyanogenic glucoside content is significantly positively correlated with the expression of CYP79D15 and CYP736A187 , which correspond to the first two steps in the cyanogenic glucoside biosynthetic pathway (Additional file 4 : Fig. S14). Surprisingly, however, no significant correlation was observed between the number of gene copies and levels of gene expression (Additional file 4 : Fig. S15); this could be an artifact of inadequate time point sampling of tissue, as regulation of cyanogenic glucoside synthesis is known to vary diurnally in other cyanogenic plant species [ 42 ].

Cyanogenesis gene copy number variation and associations with cyanogenic potential and climate. A – D Gene copy number variation and cyanide (HCN) content (per gram fresh leaf tissue). Linear regression line and standard error are plotted if the slope is significantly different from zero ( p  < 0.05). Each point stands for the mean value of 3–4 repeated measurements. E – H Association between cyanogenesis gene copy number and Mean Temperature of Coldest Quarter (MTCQ). E Ac copy number. F Li copy number. G The product of ( Ac copy number)*( Li copy number) within an accession. H Collection locations of 419 sampled accessions (43 populations) across North America. The map is shaded by MTCQ. Pearson correlation coefficient was calculated; if the value was significantly different from zero, the linear model was plotted. Raw data are available at Additional file 1 : Table S7 and Additional file 3 : Table S8

Wild populations and environmental associations

Finally, to assess whether CNVs at the Ac and Li cyanogenesis loci contribute to environmental adaptation in wild populations of white clover, we sampled 419 wild accessions from 43 white clover populations distributed across North America, estimated the copy number at the Ac and Li loci using genomic DNA qPCR, and tested for associations with climatic variables of the collection locales using the WorldClim 2 dataset [ 43 ]. CNVs for the wild population samples ranged from 0 to 8 per accession for Ac and 0 to 6 for Li (Additional file 3 : Table S8 and Additional file 4 : Fig. S16). Because previous observations of white clover cyanogenesis clines have detected strongest associations between cyanogenesis frequencies and minimum winter temperature [ 6 , 36 ], we focused on the climatic variable Mean Temperature of Coldest Quarter (MTCQ) for CNV association tests. Notably, both CYP79D15 (the first gene in the Ac cluster) and Li are significantly correlated with MTCQ (Fig.  4 E, F, H). Importantly, these correlations remained significant after removing accessions with zero copies of the genes (corresponding to the acac and lili recessive genotypes); this indicates that the significant correlations between CNVs and climate are not simply reflecting the previously described PAVs at the Ac and Li loci, which are well known to contribute to white clover cyanogenesis clines [ 6 , 36 ] (Additional file 4 : Fig. S17, Fig. S18).

Because the cyanogenic phenotype arises through an epistatic interaction between the two cyanogenesis loci, we also examined the cumulative effects of CNVs at both loci by assessing MTCQ associations with the product of CYP79D15 and Li gene copy numbers. As with the individual loci, this assessment revealed a significant positive correlation (Figs. 4 G and Additional file 4 : Fig. S17C, S18C). This finding further suggests that adaptive clinal variation at the white clover cyanogenesis loci arises through selection acting on the Ac and Li CNVs.

PacBio HiFi, Omni-C, and linkage map bridge the gap to allotetraploid genome assembly

The increased availability of high-quality long-read genomic data has contributed to our understanding of the importance of structural variation, including CNVs, as contributors to phenotypic variation. However, despite the fact that CNVs are prevalent in nature, very few studies to date have documented the distribution of CNVs in the wild and correlated these with known adaptive phenotypes along an environmental gradient. In this study, we bridge this knowledge gap by revisiting the molecular mechanism underlying the well-known cyanogenesis polymorphism in white clover using three newly available high-quality genomes generated with long-read based sequencing technology (this study; 11, 12). The genome assembly presented here is the second haplotig-resolved genome of white clover and was generated by incorporating high-depth (84 ×) PacBio HiFi sequencing, Omni-C chromatin interaction, and linkage map information. The quality of our new genome assembly highlights the value of high-density linkage map data for complementing Omni-C data in scaffolding, especially when joining two long-range scaffolds in their correct orientation (e.g., across centromeres). The ability to accurately scaffold chromosomes is often limited when using Omni-C data alone (see, e.g., comparisons of the three white clover genomes with and without linkage map assisted scaffolding; Additional file 4 : Fig. S19, Fig. S20). Our genome also represents a true de novo genome assembly for white clover — created without first assigning sequences to a subgenome before attempting the assembly. The resulting assembly thereby provides the most unbiased and reliable chromosome-level and haplotig-resolved genome to date for this economically important legume species.

Unexpectedly little genome reorganization in white clover

Polyploidization is characterized by an immediate multiplication in chromosome number through a whole genome duplication event, which can produce a major shock to the genome that may cause a series of unpredictable but discernible downstream changes [ 44 ]. These may include but are not restricted to bursts of transposable element (TE) proliferation [ 37 ], large-scale genome rearrangements [ 45 ], fractionation [ 46 ], and the accumulation of deleterious mutations due to duplicated gene redundancy [ 47 ]. In our study, we found extensive distribution of TEs in white clover genome (30% of the genome). The short branches of the phylogenetic trees of LTR retrotransposons (LTR-RTs) suggest a recent multiplication of LTR-RT elements within the white clover genome (Additional file 4 : Fig. S2, Fig. S3). It is possible that the high number of TEs in the white clover genome has facilitated the diploidization process by accumulating new mutations that contributed to subgenome divergence, thereby repressing recombination between homeologous chromosomes [ 37 ]. Further investigation of the TE family expansions, including in the diploid progenitor species, would be useful to test this hypothesis.

The genus Trifolium is characterized by extensive chromosome number variation between species and occasional polyploid speciation events [ 48 ]. In our genomic comparisons with the closely related legume Medicago truncatula , white clover shows the most conserved chromosome structure of the three Trifolium species with reference genomes, the other two of which are diploid; T. pratense (2n = 14) and T. subterraneum (2n = 16) each possess a minimum of 2–3 chromosomal rearrangements in a majority of their chromosomes (Fig.  2 C). This finding suggests that polyploidization by itself does not necessarily lead to large-scale genome reorganizations and, conversely, that extensive genome reorganization can occur in the absence of polyploidization.

Beyond the genus Trifolium , inclusion of the white clover genome in legume genome sequence comparisons is also pivotal in accurately reconstructing the karyotype evolution of the IRLC legume clade. This economically important group contains the majority of agriculturally important legumes, including chickpea, lentil, pea, vetch, alfalfa, and clovers. Notably, without the white clover genome sequence comparison, it would be impossible to tell that the basal chromosomal organization in Trifolium is conserved with related legume genera (Fig.  2 C).

Evolutionary theory predicts that species may experience a relaxation of purifying selection after polyploidization because the redundant gene copies (homeologs) are capable of masking the effect of deleterious recessive mutations [ 47 ]. We therefore expected to detect more mutations in homeologous gene pairs than in pairwise comparisons of homologous (orthologous) genes of the extant progenitors. However, no significant difference in the ratio of nonsynonymous versus synonymous mutation rate ( K A / K S ) was found (Additional file 4 : Fig. S6). Moreover, we did not detect any major genome fractionation in either subgenome (Fig.  2 A) (see also [ 9 ]). These observations together suggest that white clover has experienced little relaxation of purifying selection after the polyploidization event that created this species. Although this finding is contrary to the classic expectations for homeologous gene copies, it is possible that selection to maintain dosage balance could be preventing any such relaxation of purifying selection [ 46 ]. It is also possible that white clover’s very recent evolutionary origin (estimated at 15,000–28,000 years ago) [ 9 ] is simply too short of a time period to allow for the detectable accumulation of deleterious mutations.

Revisiting the evolution of the adaptive cyanogenesis polymorphism

Understanding the genetic and biochemical bases of the white clover cyanogenesis polymorphism, and the ecological factors that shape its evolution, has been a century-long research endeavor. Initial characterizations of the biochemical components and the polymorphism’s inheritance patterns predated the molecular era [ 49 , 50 , 51 , 52 , 53 ]. In the 1950s, a series of classic studies by Daday surveyed cyanogenesis frequencies in wild populations worldwide; this work revealed the widespread occurrence of latitudinal and elevational cyanogenesis clines throughout the native and introduced species range, with cyanogenic plants consistently present at higher frequencies in warmer climates [ 7 , 8 , 54 ]. In subsequent decades, numerous studies have examined the potential environmental factors that maintain the polymorphism and drive the evolution of climate-associated cyanogenesis clines (reviewed by [ 5 , 29 , 36 , 55 , 56 ]). Key selective factors likely include chemical defense in areas of high herbivore abundance, fitness tradeoffs associated with the energetic costs of producing cyanogenic components, and abiotic stresses including cold and drought. Complementary studies have focused on the molecular basis of the polymorphism and its evolutionary origins. This work has revealed that the Ac/ac and Li/li polymorphisms arise through two unlinked PAVs [ 30 , 31 , 32 ]; that the recessive (deletion) alleles have evolved repeatedly in white clover as well as in related Trifolium species [ 32 , 33 , 35 ]; and that the Ac and Li loci of white clover are derived from different diploid progenitors, with Ac derived from T. occidentale and Li derived from the T. pallescens lineage [ 13 ]. As the body of white clover cyanogenesis research has progressed, this system has come to be regarded as a textbook example of a balanced polymorphism maintained by geographically heterogeneous selective pressures [ 57 , 58 ].

By identifying the occurrence of CNVs for the dominant (gene-presence) alleles of the Ac and Li loci, and by documenting their associations with climatic variation in wild populations across the North American species range, the present study adds new depth to our understanding of the white clover cyanogenesis polymorphism and its role in adaptive evolution. Analysis of the three long-read genome assemblies (this study; 11, 12) revealed CNV counts of 0, 2, and 3 for the Ac locus and 2 copies for the Li locus in the haploid genomes (Fig.  3 , Additional file 4 : Fig. S10, Fig. S11). With expanded sampling to include 419 wild North American accessions, estimated CNV counts ranged from 0 to 8 per accession for Ac and 0–6 for Li (Additional file 3 : Table S8 and Additional file 4 : Fig. S16). The mechanism responsible for the producing these CNVs remains unknown; however, our genome sequence analyses suggest that it is unlikely to be due to mobile element activity. TEs are not especially abundant in the genomic regions of the Ac and Li loci, and those elements that are present do not show any detectable patterns associated with CNV counts (Additional file 4 : Fig. S21). This may suggest that other mechanisms, such as nonallelic homologous recombination (NAHR), are responsible for the observed structural variation.

The physical chromosome map produced in the present study corroborates our previous findings, based on QTL mapping of the cyanogenesis phenotype, on the genomic locations of the Ac and Li loci [ 13 ]. Interestingly, unlike most genes in the polyploid white clover genome, no functional homeologs of either Ac or Li were detected in BLASTs against all gene models (data not shown), despite the highly conserved gene synteny between the two subgenomes overall. These findings lend further support to our prior conclusions that the Ac and Li genes of white clover are uniquely derived from its two different diploid progenitors [ 13 ].

Another key finding of our study is the positive correlation between the number of CNVs at the Ac locus and cyanogenic glucoside production (Fig.  4 A–C). To our knowledge, this is the first reported evidence in any cyanogenic species of quantitative variation in cyanogenic glucoside production due to CNVs of the underlying biosynthetic pathway. In contrast to white clover, the cyanogenic glucoside content in cassava [ 59 ] and Lotus corniculatus [ 60 ] has been reported to be regulated by cell membrane transporter genes and stress responsive genes, respectively. It thus appears that quantitative control of cyanogenic glucoside accumulation in different plant lineages has evolved independently through different molecular mechanisms. The correlation between Ac CNV dosage and cyanogenic glucoside production also illustrates the importance of multiplication of the entire gene cluster. We found that each Ac CNV unit includes all three component genes of the biosynthetic pathway ( CYP79D15 , CYP736A187 , UGT85K17 ) [ 32 ] and that the gene order is conserved across CNV copies (Fig.  3 A, C; Additional file 4 : Fig. S10A, Fig. S11A). If there were multiplication of some but not all of the three genes, the imbalance and resulting accumulation of unstable chemical intermediates could potentially be maladaptive or even toxic [ 32 , 61 , 62 ]. To our knowledge, only one other study has documented the role of variable copies of an entire gene cluster in contributing to phenotypic variation (soybean nematode resistance locus Rhg1 ) [ 19 ].

Finally, our results demonstrate that CNVs contribute to local adaptation and the evolution of climate-associated adaptive clines in wild populations of a plant species. As noted above, the white clover cyanogenesis polymorphism has long served as a model for understanding the forces that generate and maintain adaptive polymorphisms within species. Our findings here that CNVs in wild white clover populations are significantly correlated with local climate (Fig.  4 E–H), together with the evidence that CNVs directly affect the cyanogenic phenotype (Fig.  4 A–C), provide a new level of resolution to our understanding of how natural selection interacts with the genome to shape this adaptive polymorphism. To our knowledge, these results provide the first extensively documented case of CNV-mediated local environmental adaptation in a wild plant species. Equally importantly, they highlight the value of long-read sequencing technologies in accurately capturing the full spectrum of genomic variants that contribute to adaptation. The last decade has witnessed a rapid growth in studies using whole genome sequencing to assess molecular basis of adaptation in plants and many other species [ 63 , 64 ]. However, reliance on short-read data and alignments to a single reference genome has meant that discoveries are heavily biased towards SNP variants, with little knowledge of the role that CNVs or other structural variants (SVs) may play in adaptation. While population-scale pangenome sequencing is still economically unfeasible for nearly all eukaryotic systems, a hybrid approach such as employed in our study (i.e., using a low-sample pangenome to discover and validate SVs, followed by qPCR to conduct population-scale investigations) could be an affordable way to uncover the role of SVs in adaptation.

Through analyses based on a newly generated, de novo assembled haplotype-resolved genome for white clover, we are able to draw several key inferences about the genomic composition and evolution of this economically important species: (1) the allotetraploid white clover genome, while characterized by recently elevated TE activities, retains highly conserved genome organization and gene composition, with no evidence of relaxed purifying selection in homeologs since its origin; (2) unlike its two diploid congeners with reference genomes ( T. pratense and T. subterraneum ), both of which have undergone extensive chromosomal reorganizations, white clover has preserved a more ancestral genome structure that is shared with other members of the IRLC legume clade; (3) the Ac and Li cyanogenesis loci are both characterized by CNVs, which contribute to quantitative variation in the cyanogenic phenotype and to local climatic adaptation in wild populations spanning the North American species range. Finally, this study highlights the value of integrating PacBio long-read sequencing, Omni-C chromatin interaction, and linkage map data in polyploid genome assembly and the power of pangenome perspectives in identifying the functional effects of structural variation and its role in local adaptation in nature.

De novo genome assembly

Young leaf tissue of a wild North American white clover accession “GFL_007” (grown from seed collected in Gainesville, Florida; NCBI SAMN37329216) was dark-treated for 48 h before DNA isolation. High molecular weight (HMW) DNA was isolated by Polar Genomics (Ithaca, NY) using a CsCl gradient to remove mitochondrial and chloroplast genomes. The HMW DNA was then submitted to the Roy J. Carver Biotechnology Center (University of Illinois at Urbana-Champaign, Urbana, IL 61801) for PacBio HiFi sequencing. The HMW DNA was sheared with a Megaruptor 3 to a size of ~ 15 Kbp. Sheared gDNA fragments were converted to a library with the SMRTBell Express Template Prep kit 2.0. The library was sequenced on three SMRTcell 8 M on a PacBio Sequel II using the circular consensus sequencing (CCS) sequencing mode and a 30-h movie time. CCS analysis was performed using SMRTLink V8.0 using the following parameters: ccs –min-length 1000 –max-length 50000 –min-passes 3 –min-rq 0.99. By this approach, PacBio HiFi generated 7.3 million reads with average length 11.6 Kbp. The expected mean coverage is 77 × for a collapsed haploid genome based on the sequencing depth and previously reported genome size [ 9 ]. In order to phase the diploid genome and scaffold, Omni-C was used to capture the chromatin proximity interactions. The Omni-C library and sequencing services were also provided by the Roy J. Carver Biotechnology Center. One hundred million read pairs (2 × 150 bp) were sequenced by using NovaSeq 6000 System with SP flow cell (Illumina). The raw reads were demultiplexed and the adaptors were trimmed using the Illumina standard software.

Although the extant white clover genome is likely to have minimal inter-subgenome translocations (e.g., homeologous recombination) [ 9 ], we cannot exclude that translocations might have occurred following polyploidization. Therefore, unlike the most recent previous assembly [ 12 ], which categorized the PacBio HiFi reads into subgenomes before final assembly, we conducted a complete de novo assembly from the raw PacBio HiFi reads. Hifiasm v0.16.1 was used for the de novo genome assembly with Hi-C integrated mode [ 65 ]. The output of the fully phased contigs of the primary and alternative haplotigs were used as the template for Omni-C scaffolding independently. The adaptor-trimmed Omni-C reads were mapped to the two haplotigs independently according to the mapping pipeline provided by Arima Genomics [ 66 ]. In short, the pipeline first maps the reads to the reference in single-end mode. Then, the chimeric mapped reads (if present) were processed to retain only the 5′-end of the sequence (on the assumption that the 3′-end was a result of proximal ligation). Finally, the paired reads were joined and sorted in the output.

The genome scaffolding was conducted in two steps. First, the Hi-C scaffolding was performed by SALSA2 with the default settings [ 67 ]. Second, two linkage maps created from two F 2 mapping populations (see the “ Linkage map construction ” section) were used to place the Hi-C scaffolds into pseudochromosomes. The two linkage maps were merged based on the physical locations of the markers in the corresponding contigs, and the genetic distances (cM) were re-estimated using either the mean genetic distance of the two linkage maps or the only available value if the marker was only present in one linkage map. Chromonomer was used to integrate the information of the Hi-C scaffolding and the linkage map and then generate the final genome assembly information file (AGP file) [ 68 ]. Then, agptools was used to update the final assembly [ 69 ]. The chromosome numbers were labeled by the BLAST against the diploid progenitors’ genome (see subgenome characterization) and the nucleotide alignment to Medicago truncatula (see gene synteny and nucleotide alignment). The complete chloroplast and the partial mitochondrial genomes were assembled from the Illumina reads in GetOrganelle [ 70 ]. Then, the chloroplast and mitochondrial genome, plus the NCBI prokaryotic RefSeq genomes were used as the reference in BLAST to remove the contaminations from the nuclear genome.

k-mer based genome quality assessment

To estimate the reference-free genome size and heterozygosity before assembly, an independent Illumina short read (2 × 150 bp) dataset of the same accession (GFL_007; SAMN37329216) was used to generate a histogram of k-mer frequencies in jellyfish with default settings (k-mer length of 21) [ 71 ]. Then, the estimation was performed by GenomeScope [ 72 ]. To assess the assembly quality, the same Illumina dataset was used and the assessment was performed by Merqury [ 73 ].

Linkage map construction

Two linkage maps were used to scaffold and assess the scaffolding quality of the new assembly. These were generated from two F 2 mapping populations derived from biparental crosses of GFL_007 and two other white clover genotypes [DG population from a cross of accessions DMN_010 (SAMN34157026) and GFL_007 (SAMN37329216), GS population from accessions GFL_007 (SAMN37329216), and STL_0701 (SAMN34157027)] [ 13 , 74 ]. Protocols for generation of genotyping-by-sequencing (GBS) marker data used in linkage map construction are described in [ 13 ]. For the present study, de novo linkage map construction was performed by mapping raw GBS reads to the two Omni-C scaffolded haplotigs; SNPs were called independently following the GATK best practice workflow [ 75 ]. In brief, the reads were mapped to the reference by Bowtie2 (bowtie2 –sensitive –no-mixed –no-discordant –minins 100 –maxins 1000). Then, the SNPs were called by HaplotypeCaller (-ERC GVCF) and combined by GenotypeGVCFs. The output in vcf format was first hard-filtered (bcftools filter -e 'QD < 0.5 || FS > 200.0 || MQ < 20.0 || MQRankSum < -12.5 || MQRankSum > 12.5 || ReadPosRankSum < -8.0 || SOR > 8 || INFO/DP < 2000'); then, individuals with missing genotype (SNP site) > 0.25 were removed and then filtered for minor allele frequency > 0.35, max number of alleles ≤ 2, p -value > 1e − 20 on a genotype frequency test (indicating no significant deviation from 1:2:1 segregation in the F 2 populations), minimum mean depth > 15, and missing data < 0.25; finally, only genotypes (SNP sites) that were homozygous in the parents were preserved. The linkage maps were then constructed by GUSMap package with the standard protocol with the minimum LOD threshold that can form 16 linkage groups [ 76 ]. The quality of the final phased genome assembly (primary haplotig) was further validated by serving as the reference for mapping the raw GBS reads. All procedures for raw read mapping and follow-up steps were the same as for linkage map construction, except for that all the individuals of the mapping populations were kept. The final vcf files were converted to the “cross” format and imported to the R/qtl package [ 77 ]. Then, the heatmaps of the LOD scores and the recombination fractions between the pairwise markers were visualized.

Subgenome categorization

In order to characterize the subgenome identities of either T. occidentale or T. pallescens origin, the final assembly (primary and alternative haplotigs) was used as the query to BLAST (which allows multiple alignment hits to ambiguous genomic regions) against published genomes of the two diploid progenitors, T. occidentale and T. pallescens [ 9 ]. Only the hits with alignment > 1000 bp and identity > 95% were kept. Then, a customized index was calculated based on: \(\sum\,{(To\,alignment\,length)}_{i }-\sum {(Tp\,alignment\,length)}_{k}/Bin\,size\,(0.5\,Mb)\) , where i and k are the filtered hits to either the T. occidentale (To) or T. pallescens (Tp) subgenome, respectively. This measure creates an index where a value of -1 is completely T. pallescens -like and + 1 is completely T. occidentale -like.

Gene prediction and functional annotation

The two haplotigs of the diploid assembly were independently annotated with the BRAKER2 pipeline [ 78 ]. The haploid genome was first softmasked for the repeated regions by RepeatModeler v2.0.4 and RepeatMasker v4.1.4 with a de novo constructed and lineage-specific database. Then, the gene model was separately predicted with RNA-seq and protein data. The results were then combined by TSEBRA as suggested [ 79 ]. The RNA-seq data were derived from leaf tissue samples of the three accessions used to create the linkage maps (DMN_010, GFL_007, STL_0701); tissue was collected from plants grown in the greenhouse under control and drought treatments ( N  = 18, including three accessions and three clonal replications of each accession). The RNA-seq reads were mapped to the reference in a relaxed mode (–outFilterScoreMinOverLread 0 –outFilterMatchNminOverLread 0) by STAR v2.7.10a [ 80 ]. The protein data were from the translated sequences of the previous published white clover genome [ 9 ] and OrthoDB v10 “Fabales” database. For functional annotation, the primary transcripts of each gene model were translated into protein sequences and analyzed using the blastp algorithm against the NCBI RefSeq genomes of Arabidopsis, soybean ( Glycine max ), and Medicago truncatula . The BLAST result was then imported in Blast2GO software. After combining the results from InterProScan, the GO terms were mapped and annotated following standard protocols [ 81 ].

LTR retrotransposons (LTR-RTs) characterization

LTR-RTs were detected and classified by inpactor2 [ 82 ]. The functional components of LTR-RT were then classified by TEsorter [ 83 ] with REXdb database [ 84 ]. The reverse transcriptase (RT) amino acid sequences were extracted and aligned by MAFFT [ 85 ]. Maximum likelihood tree was reconstructed by IQ-TREE2 [ 86 ].

Gene synteny and nucleotide alignment

Interspecific gene synteny analysis was conducted for related legume species by GENESPACE v0.9.3 [ 87 ], which integrates the results from OrthoFinder [ 88 ] and MCscanX [ 89 ]. In short, the primary transcripts of Trifolium pratense [ 90 ], Trifolium subterraneum [ 91 ], Medicago truncatula [ 92 ], Cicer arietinum [ 93 ], Lotus japonicus [ 94 ], and Phaseolus vulgaris [ 95 ] were used as the input in OrthoFinder in nucleotide mode. Then, the gene synteny was generated by MCscanX with the settings: blkSize = 20, nGaps = 25. Nucleotide alignment was conducted by Mummer 4.0.0 with the function: nucmer [ 96 ]. Only the alignment hits with more than 1000 bp were kept. The gene synteny and nucleotide alignment were then visualized using package gggenomes v0.9.5 in R.

Synonymous mutation rate (K S ) and nonsynonymous mutation rate (K A )

To assess the divergence among orthologs across species and examine subgenome-specific patterns of molecular evolution within white clover (e.g., relaxed purifying selection), synonymous mutation rate ( K S ) and nonsynonymous mutation rate ( K A ) were calculated based on the single copy orthologous genes that are universally available in all the taxa in the synteny analysis (except for Trifolium subterraneum ). After filtering, 3884 genes were kept. The orthologous gene sequences were aligned and guided by their translated protein sequences in MACSE v2 [ 97 ]. The low-quality alignment was removed following the OMM_MACSE pipeline. The cleaned alignments were then used for mutation rate calculation in KaKs_Calculator 2.0 with YN method [ 98 ].

Cyanogenesis gene copy number variation, gene expression and cyanide quantification

Plant materials used to characterize cyanogenesis variation were collected as seeds or stolons from North American wild populations and cultivated in the greenhouse of Washington University in St. Louis (Additional file 1 : Table S7, S8). The cyanogenic phenotype, including the presence/absence of the cyanogenic glucosides and the substrate-specific glucosidase, was assessed biochemically by Feigl-Anger cyanogenesis assay and genetically by PCR-genotyping of CYP79D15 (the first gene in the Ac metabolic cluster) and Li [ 30 , 31 ]. Plants that produced one or both cyanogenic precursors were further examined by qPCR with gDNA to estimate the gene copy number information and with cDNA to estimate the relative gene expression level. The qPCR primers of CYP79D15 , CYP736A187 , and UGT85K17 (together comprising the Ac locus) and Li were designed based on the sequences in our previous publications [ 30 , 32 ] and are available in Additional file 1 : Table S9. The qPCR was conducted in the CFX96 Real-Time PCR Detection System (BIO-RAD), and the data were analyzed by ΔΔC T method with efficiency calibration in CFX Maestro™ Software (BIO-RAD) according to the manufacturer’s instructions. At least three independent qPCR replicates were conducted for each gene. In each independent replicate, three technical replicates were included. The C T value was first normalized to Ef1α gene and then scaled to the normalized C T value of the single-copy genotype. To provide a reference genotype for quantification of gene copy numbers, accessions possessing a single copy of each assayed gene were identified in pedigree populations created by crossing cyanogenic and acyanogenic parents (Additional file 2 : Table S3).

The cyanogenic glucoside content per unit weight of the fresh leaf tissue was measured by a colorimetric method. Leaf tissue (1 g) was homogenized in liquid nitrogen and resuspended in 2 mL of Na 2 HPO 4 buffer (0.067 M, pH 6.0). Then, the sample was centrifuged at 4 °C for 20 min at 13,000 × g twice. After each centrifugation, the supernatant was transferred to a new tube. Then, an excess amount of linamarase (100 μL bulk protein extract from cassava latex, [ 99 ]) was added to the sample (50 μL supernatant + 450 μL Na 2 HPO 4 buffer) and incubated 30 min at 37 °C to release cyanide from cyanogenic glucosides (linamarin and lotaustralin). After incubation, 600 μL NaOH solution (0.2 M) was added to terminate the reaction. Then, 500 μL of the reaction mix was used for cyanide quantification by Spectroquant® Cyanide Test kit (Millipore cat. no. 1.09701) following the standard protocol. The standard cyanide solution was prepared by the same procedure but replaced the supernatant with the commercial cyanide standard (Millipore cat. no. 119533). The colorimetric reaction had the maximum absorbance at 607 nm. We used the absorbances at 700 nm and 775 nm to estimate the background absorbance at 607 nm by simple linear regression. Finally, the absorbance of 607 nm was subtracted by the background, and then compared to the standard curve for concentration.

Environmental association

To investigate the association between the cyanogenesis gene copy number variation and environmental factors, climatic information at 1-km resolution was downloaded from WorldClim 2 [ 43 ]. We extracted the environmental information for collection locations of the sampled wild clover populations as the mean value from the 1000-m-buffered sample location. Then, the gene copy number was correlated to Mean Temperature of Coldest Quarter (MTCQ) with Pearson correlation method and visualized in R.

Availability of data and materials

All data generated or analyzed during this study are included in this published article, its supplementary information files and publicly available repositories. The de novo genome assemby datasets generated during the current study have been deposited with links to BioProject accession number PRJNA953427 in the NCBI BioProject database ( https://www.ncbi.nlm.nih.gov/bioproject/ ). It includes the PacBio HiFi raw reads (SRR24107384), the RNA-seq (SRR24147954–SRR24147971), the GBS adapter-trimmed reads of the GS F 2 mapping population (SRR24400943–SRR24401442), the GBS adapter-trimmed reads of the DG F 2 mapping population (SRR24330286–SRR24330790), Omni-C raw reads (SRR24133794), and the assembled genome and annotation (GCA_032173615.1; JAVQLX000000000). The shotgun resequencing datasets generated during the current study have been deposited to NCBI BioProject database (PRJNA1064563). The raw data for cyanogenesis analyses are available at Additional file 1 : Table S7 and Additional file 3 : Table S8.

Abbreviations

Inverted repeat-lacking clade

Copy number variation

Presence/absence variations

Transposable element

Coding sequence

Mean Temperature of Coldest Quarter

LTR retrotransposons

Structural variants

High molecular weight DNA

Circular consensus sequencing

Genotyping-by-sequencing

Zeven AC. Four hundred years of cultivation of Dutch white clover landraces. Euphytica. 1991;54(1):93–9.

Article   Google Scholar  

Taylor NL. A century of clover breeding developments in the united states. Crop Sci. 2008;48(1):1–13.

Abberton MT, Marshall AH. White clover. In: Boller B, Posselt UK, Veronesi F, editors. Fodder crops and amenity grasses. New York: NY: Springer New York; 2010. p. 457–76.

Chapter   Google Scholar  

Kjærgaard T. A plant that changed the world: the rise and fall of clover 1000–2000. Landsc Res. 2003;28(1):41–9.

Santangelo JS, Ness RW, Cohan B, Fitzpatrick CR, Innes SG, Koch S, et al. Global urban environmental change drives adaptation in white clover. Science. 2022;375(6586):1275–81.

Article   CAS   PubMed   Google Scholar  

Kooyers NJ, Olsen KM. Searching for the bull’s eye: agents and targets of selection vary among geographically disparate cyanogenesis clines in white clover ( Trifolium repens L.). Heredity. 2013;111(6):495–504.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Daday H. Gene frequencies in wild populations of Trifolium repens L. Distribution by latitude Heredity. 1954;8(1):61–78.

Google Scholar  

Daday H. Gene frequencies in wild populations of Trifolium repens . II Distribution by altitude Heredity. 1954;8(3):377–84.

Griffiths AG, Moraga R, Tausen M, Gupta V, Bilton TP, Campbell MA, et al. Breaking free: the genomics of allopolyploidy-facilitated niche expansion in white clover. Plant Cell. 2019;31(7):1466–87.

Ravagnani A, Abberton MT, Skøt L. Development of genomic resources in the species of Trifolium L. and its application in forage legume breeding. Agronomy. 2012;2(2):116–31.

Article   CAS   Google Scholar  

Wang H, Wu Y, He Y, Li G, Ma L, Li S, et al. High-quality chromosome-level de novo assembly of the Trifolium repens . BMC Genomics. 2023;24(1):326.

Santangelo JS, Battlay P, Hendrickson BT, Kuo WH, Olsen KM, Kooyers NJ, et al. Haplotype-resolved, chromosome-level assembly of white clover ( Trifolium repens L., Fabaceae). Genome Biol Evol. 2023;15(8):2023.06.06.543960.

Olsen KM, Goad DM, Wright SJ, Dutta ML, Myers SR, Small LL, et al. Dual-species origin of an adaptive chemical defense polymorphism. New Phytol. 2021;232(3):1477–87.

Alonge M, Wang X, Benoit M, Soyk S, Pereira L, Zhang L, et al. Major impacts of widespread structural variation on gene expression and crop improvement in tomato. Cell. 2020;182(1):145–61e23.

Qin P, Lu H, Du H, Wang H, Chen W, Chen Z, et al. Pan-genome analysis of 33 genetically diverse rice accessions reveals hidden genomic variations. Cell. 2021;184(13):3542–58.e16.

Zmienko A, Marszalek-Zenczak M, Wojciechowski P, Samelak-Czajka A, Luczak M, Kozlowski P, et al. AthCNV: a map of DNA copy number variations in the Arabidopsis genome. Plant Cell. 2020;32(6):1797–819.

Zhuang W, Chen H, Yang M, Wang J, Pandey MK, Zhang C, et al. The genome of cultivated peanut provides insight into legume karyotypes, polyploid evolution and crop domestication. Nat Genet. 2019;51(5):865–76.

Pos O, Radvanszky J, Buglyo G, Pos Z, Rusnakova D, Nagy B, et al. DNA copy number variation: main characteristics, evolutionary significance, and pathological aspects. Biomed J. 2021;44(5):548–59.

Cook DE, Lee TG, Guo X, Melito S, Wang K, Bayless AM, et al. Copy number variation of multiple genes at Rhg1 mediates nematode resistance in soybean. Science. 2012;338(6111):1206–9.

Wang Y, Xiong G, Hu J, Jiang L, Yu H, Xu J, et al. Copy number variation at the GL7 locus contributes to grain size diversity in rice. Nat Genet. 2015;47(8):944–8.

Wright D, Boije H, Meadows JR, Bed’hom B, Gourichon, Vieaud A, et al. Copy number variation in intron 1 of SOX5 causes the Pea-comb phenotype in chickens. PLoS Genet. 2009;5(6):e1000512.

Article   PubMed   PubMed Central   Google Scholar  

Stalder L, Oggenfuss U, Mohd-Assaad N, Croll D. The population genetics of adaptation through copy number variation in a fungal plant pathogen. Mol Ecol. 2023;32(10):2443–60.

Wang X, Li MW, Wong FL, Luk CY, Chung CYL, Yung WS, et al. Increased copy number of gibberellin 2-oxidase 8 genes reduced trailing growth and shoot length during soybean domestication. Plant J. 2021;107(6):1739–55.

Díaz A, Zikhali M, Turner AS, Isaac P, Laurie DA. Copy number variation affecting the Photoperiod-B1 and Vernalization-A1 genes is associated with altered flowering time in wheat (Triticum aestivum). PLoS ONE. 2012;7(3):e33234.

Kreiner JM, Hnatovska S, Stinchcombe JR, Wright SI. Quantifying the role of genome size and repeat content in adaptive variation and the architecture of flowering time in Amaranthus tuberculatus . PLoS Genet. 2023;19(12):e1010865.

Ishikawa A, Stuart YE, Bolnick DI, Kitano J. Copy number variation of a fatty acid desaturase gene Fads2 associated with ecological divergence in freshwater stickleback populations. Biol Lett. 2021;17(8):20210204.

Ishikawa A, Kabeya N, Ikeya K, Kakioka R, Cech JN, Osada N, et al. A key metabolic gene for recurrent freshwater colonization and radiation in fishes. Science. 2019;364(6443):886–9.

DeBolt S. Copy number variation shapes genome diversity in arabidopsis over immediate family generational scales. Genome Biol Evol. 2010;2:441–53.

Hughes MA. The cyanogenic polymorphism in Trifolium repens L. (white clover). Heredity. 1991;66(1):105–15.

Olsen KM, Sutherland BL, Small LL. Molecular evolution of the Li/li chemical defence polymorphism in white clover (Trifolium repens L.). Mol Ecol. 2007;16(19):4180–93.

Olsen KM, Hsu SC, Small LL. Evidence on the molecular basis of the Ac/ac adaptive cyanogenesis polymorphism in white clover (Trifolium repens L.). Genetics. 2008;179(1):517–26.

Olsen KM, Small LL. Micro- and macroevolutionary adaptation through repeated loss of a complete metabolic pathway. New Phytol. 2018;219(2):757–66.

Olsen KM, Kooyers NJ, Small LL. Adaptive gains through repeated gene loss: parallel evolution of cyanogenesis polymorphisms in the genus Trifolium (Fabaceae). Philos Trans R Soc Lond B Biol Sci. 2014;369:(1648).

Kooyers NJ, Olsen KM. Adaptive cyanogenesis clines evolve recurrently through geographical sorting of existing gene deletions. J Evol Biol. 2014;27(11):2554–8.

Olsen KM, Kooyers NJ, Small LL. Recurrent gene deletions and the evolution of adaptive cyanogenesis polymorphisms in white clover (Trifolium repens L.). Mol Ecol. 2013;22(3):724–38.

Kooyers NJ, Olsen KM. Rapid evolution of an adaptive cyanogenesis cline in introduced North American white clover (Trifolium repens L.). Mol Ecol. 2012;21(10):2455–68.

Article   PubMed   Google Scholar  

Parisod C, Alix K, Just J, Petit M, Sarilar V, Mhiri C, et al. Impact of transposable elements on the organization and function of allopolyploid genomes. New Phytol. 2010;186(1):37–45.

Hand ML, Ponting RC, Drayton MC, Lawless KA, Cogan NO, Charles Brummer E, et al. Identification of homologous, homoeologous and paralogous sequence variants in an outbreeding allopolyploid species based on comparison with progenitor taxa. Mol Genet Genomics. 2008;280(4):293–304.

Zhao Y, Zhang R, Jiang K-W, Qi J, Hu Y, Guo J, et al. Nuclear phylotranscriptomics and phylogenomics support numerous polyploidization events and hypotheses for the evolution of rhizobial nitrogen-fixing symbiosis in Fabaceae. Mol Plant. 2021;14(5):748–73.

Kongsaeree PT, Ratananikom K, Choengpanya K, Tongtubtim N, Sujiwattanarat P, Porncharoennop C, et al. Substrate specificity in hydrolysis and transglucosylation by family 1 β-glucosidases from cassava and Thai rosewood. J Mol Catal B Enzym. 2010;67(3):257–65.

McMahon J, Sayre R, Zidenga T. Cyanogenesis in cassava and its molecular manipulation for crop improvement. J Exp Bot. 2021;73(7):1853–67.

Schmidt FB, Cho SK, Olsen CE, Yang SW, Møller BL, Jørgensen K. Diurnal regulation of cyanogenic glucoside biosynthesis and endogenous turnover in cassava. Plant Direct. 2018;2(2):e00038.

Fick SE, Hijmans RJ. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol. 2017;37(12):4302–15.

McClintock B. The significance of responses of the genome to challenge. Science. 1984;226(4676):792–801.

Chen ZJ, Ni Z. Mechanisms of genomic rearrangements and gene expression changes in plant polyploids. BioEssays. 2006;28(3):240–52.

Cheng F, Wu J, Cai X, Liang J, Freeling M, Wang X. Gene retention, fractionation and subgenome differences in polyploid plants. Nat Plants. 2018;4(5):258–68.

Otto SP. The evolutionary consequences of polyploidy. Cell. 2007;131(3):452–62.

Ellison NW, Liston A, Steiner JJ, Williams WM, Taylor NL. Molecular phylogenetics of the clover genus ( Trifolium —Leguminosae). Mol Phylogenet Evol. 2006;39(3):688–705.

Coop I. Cyanogenesis in white clover (Trifolium repens L.) III. A study of linamarase, the enzyme which hydrolyses lotaustralin. N Z J Sci Technol Sect B. 1940;22:71–83.

Melville J, Doak B. Cyanogenesis in white clover (Trifolium repens L.) II. isolation of the gluco-sidal constituents. N Z J Sci Technol Sect B. 1940;22:67–71.

Corkill L. Cyanogenesis in white clover (Trifolium repens L.) V. The inheritance of cyanogenesis. N Z J Sci Technol Sect B. 1942;23:178–93.

Armstrong HE, Armstrong EF, Horton E. Herbage Studies. II-Variation in Lotus corniculatus and Trifolium repens (Cyanophoric plants). Proc Natl Acad Sci U S A. 1913;86(587):262–9.

CAS   Google Scholar  

Ware WM. Experiments and observations on forms and strains of Trifolium repens L. J Agric Sci. 1925;15(1):47–67.

Daday H. Gene frequencies in wild populations of Trifolium repens L. III World distribution Heredity. 1958;12(2):169–84.

Kooyers NJ, Hartman Bakken B, Ungerer MC, Olsen KM. Freeze-induced cyanide toxicity does not maintain the cyanogenesis polymorphism in white clover ( Trifolium repens ). Am J Bot. 2018;105(7):1224–31.

Kooyers NJ, Gage LR, Al-Lozi A, Olsen KM. Aridity shapes cyanogenesis cline evolution in white clover (Trifolium repens L.). Mol Ecol. 2014;23(5):1053–70.

Briggs D, Walters SM. Plant variation and evolution. 4th ed. Cambridge: Cambridge University Press; 2016.

Book   Google Scholar  

Futuyma DJ, Kirkpatrick M. Evolution. Fourth edition ed. Sunderland, Massachusetts: Sinauer Associates, Inc., Publishers Sunderland, Massachusetts; 2017.

Ogbonna AC, Braatz de Andrade LR, Rabbi IY, Mueller LA, Jorge de Oliveira E, Bauchet GJ. Large-scale genome-wide association study, using historical data, identifies conserved genetic architecture of cyanogenic glucoside content in cassava ( Manihot esculenta Crantz) root. Plant J. 2021;105(3):754–70.

Chen C, Zhang K, Liu F, Wang X, Yao Y, Niu X, et al. Resequencing of global Lotus corniculatus accessions reveals population distribution and genetic loci, associated with cyanogenic glycosides accumulation and growth traits. BMC Biol. 2023;21(1):176.

Kristensen C, Morant M, Olsen CE, Ekstrom CT, Galbraith DW, Moller BL, et al. Metabolic engineering of dhurrin in transgenic Arabidopsis plants with marginal inadvertent effects on the metabolome and transcriptome. Proc Natl Acad Sci U S A. 2005;102(5):1779–84.

Takos AM, Rook F. Why biosynthetic genes for chemical defense compounds cluster. Trends Plant Sci. 2012;17(7):383–8.

Stapley J, Reger J, Feulner PGD, Smadja C, Galindo J, Ekblom R, et al. Adaptation genomics: the next generation. Trends Ecol Evol. 2010;25(12):705–12.

Song B, Ning W, Wei D, Jiang M, Zhu K, Wang X, et al. Plant genome resequencing and population genomics: current status and future prospects. Mol Plant. 2023;16(8):1252–68.

Cheng H, Concepcion GT, Feng X, Zhang H, Li H. Haplotype-resolved de novo assembly using phased assembly graphs with hifiasm. Nat Methods. 2021;18(2):170–5.

Arima-HiC Mapping Pipeline: Arima Genomics, Inc.; 2019 [Available from: https://github.com/ArimaGenomics/mapping_pipeline .

Ghurye J, Rhie A, Walenz BP, Schmitt A, Selvaraj S, Pop M, et al. Integrating Hi-C links with assembly graphs for chromosome-scale assembly. PLoS Comput Biol. 2019;15(8):e1007273.

Catchen J, Amores A, Bassham S. Chromonomer: a tool set for repairing and enhancing assembled genomes through integration of genetic maps and conserved synteny. G3. 2020;10(11):4115–28.

Rice E. agptools: tools for working with agp files [Available from: https://warrenlab.github.io/agptools/ .

Jin J-J, Yu W-B, Yang J-B, Song Y, Depamphilis CW, Yi T-S, et al. GetOrganelle: a fast and versatile toolkit for accurate de novo assembly of organelle genomes. Genome Biol. 2020;21:241. https://doi.org/10.1186/s13059-020-02154-5 .

Marçais G, Kingsford C. A fast, lock-free approach for efficient parallel counting of occurrences of k -mers. Bioinformatics. 2011;27(6):764–70.

Vurture GW, Sedlazeck FJ, Nattestad M, Underwood CJ, Fang H, Gurtowski J, et al. GenomeScope: fast reference-free genome profiling from short reads. Bioinformatics. 2017;33(14):2202–4.

Rhie A, Walenz BP, Koren S, Phillippy AM. Merqury: reference-free quality, completeness, and phasing assessment for genome assemblies. Genome Biol. 2020;21:245. https://doi.org/10.1186/s13059-020-02134-9 .

Wright SJ, Goad DM, Gross BL, Muñoz PR, Olsen KM. Genetic trade-offs underlie divergent life history strategies for local adaptation in white clover. Mol Ecol. 2022;31(14):3742–60.

Poplin R, Ruano-Rubio V, Depristo MA, Fennell TJ, Carneiro MO, Van Der Auwera GA, et al. Scaling accurate genetic variant discovery to tens of thousands of samples. bioRxiv. 2017. https://doi.org/10.1101/201178 .

Bilton TP, Schofield MR, Black MA, Chagné D, Wilcox PL, Dodds KG. Accounting for errors in low coverage high-throughput sequencing data when constructing genetic maps using biparental outcrossed populations. Genetics. 2018;209(1):65–76.

Broman KW, Wu H, Sen Ś, Churchill GA. R/qtl: QTL mapping in experimental crosses. Bioinformatics. 2003;19(7):889–90.

Brůna T, Hoff KJ, Lomsadze A, Stanke M, Borodovsky M. BRAKER2: automatic eukaryotic genome annotation with GeneMark-EP+ and AUGUSTUS supported by a protein database. NAR Genom Bioinform. 2021;3(1). https://doi.org/10.1093/nargab/lqaa108 .

Gabriel L, Hoff KJ, Brůna T, Borodovsky M, Stanke M. TSEBRA: transcript selector for BRAKER. BMC Bioinformatics. 2021;22:566. https://doi.org/10.1186/s12859-021-04482-0 .

Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2012;29(1):15–21.

Conesa A, Götz S, García-Gómez JM, Terol J, Talón M, Robles M. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005;21(18):3674–6.

Orozco-Arias S, Humberto Lopez-Murillo L, Candamil-Cortés MS, Arias M, Jaimes PA, Rossi Paschoal A, et al. Inpactor2: a software based on deep learning to identify and classify LTR-retrotransposons in plant genomes. Brief Bioinform. 2023;24(1):bbac511. https://doi.org/10.1093/bib/bbac511 .

Zhang R-G, Li G-Y, Wang X-L, Dainat J, Wang Z-X, Ou S, et al. TEsorter: an accurate and fast method to classify LTR-retrotransposons in plant genomes. Hortic Res. 2022;9:uhac017. https://doi.org/10.1093/hr/uhac017 .

Neumann P, Novák P, Hoštáková N, Macas J. Systematic survey of plant LTR-retrotransposons elucidates phylogenetic relationships of their polyprotein domains and provides a reference for element classification. Mob DNA. 2019;10(1):1.

Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30(4):772–80.

Minh BQ, Schmidt HA, Chernomor O, Schrempf D, Woodhams MD, von Haeseler A, et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol Biol Evol. 2020;37(5):1530–4.

Lovell JT, Sreedasyam A, Schranz ME, Wilson M, Carlson JW, Harkess A, et al. GENESPACE tracks regions of interest and gene copy number variation across multiple genomes. eLife. 2022;11:e78526. https://doi.org/10.7554/eLife.78526 .

Emms DM, Kelly S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 2019;20(1):238.

Wang Y, Tang H, Debarry JD, Tan X, Li J, Wang X, et al. MCScanX: a toolkit for detection and evolutionary analysis of gene synteny and collinearity. Nucleic Acids Res. 2012;40(7):e49.e.

Bickhart DM, Koch LM, Smith TPL, Riday H, Sullivan ML. Chromosome-scale assembly of the highly heterozygous genome of red clover (Trifolium pratense L.), an allogamous forage crop species. Gigabyte. 2022;2022:1–13.

Shirasawa K, Moraga R, Ghelfi A, Hirakawa H, Nagasaki H, Ghamkhar K, et al. An improved reference genome for Trifolium subterraneum L. provides insight into molecular diversity and intra-specific phylogeny. Front Plant Sci. 2023;14:1103857.

Pecrix Y, Staton SE, Sallet E, Lelandais-Brière C, Moreau S, Carrère S, et al. Whole-genome landscape of Medicago truncatula symbiotic genes. Nat Plants. 2018;4(12):1017–25.

Varshney RK, Song C, Saxena RK, Azam S, Yu S, Sharpe AG, et al. Draft genome sequence of chickpea ( Cicer arietinum ) provides a resource for trait improvement. Nat Biotechnol. 2013;31(3):240–6.

Li H, Jiang F, Wu P, Wang K, Cao Y. A high-quality genome sequence of model legume Lotus japonicus (MG-20) provides insights into the evolution of root nodule symbiosis. Genes. 2020;11(5):483.

Schmutz J, McClean PE, Mamidi S, Wu GA, Cannon SB, Grimwood J, et al. A reference genome for common bean and genome-wide analysis of dual domestications. Nat Genet. 2014;46(7):707–13.

Marçais G, Delcher AL, Phillippy AM, Coston R, Salzberg SL, Zimin A. MUMmer4: a fast and versatile genome alignment system. PLoS Comput Biol. 2018;14(1):e1005944.

Ranwez V, Douzery EJP, Cambon C, Chantret N, Delsuc F. MACSE v2: toolkit for the alignment of coding sequences accounting for frameshifts and stop codons. Mol Biol Evol. 2018;35(10):2582–4.

Wang D, Zhang Y, Zhang Z, Zhu J, Yu J. Kaks_calculator 2.0: a toolkit incorporating gamma-series methods and sliding window strategies. Genomics Proteomics Bioinformatics. 2010;8(1):77–80.

Haque MR, Bradbury JH. Preparation of linamarase solution from cassava latex for use in the cassava cyanide kit. Food Chem. 1999;67(3):305–9.

Download references

Acknowledgements

We thank the high school Biology teachers, plant scientists, and others who participated in the crowd-sourced collection of wild white clover accessions across North America, including Miranda Colletta, Sunita Crittenden, Felix Fritschi, Briana Gross, Nic Kooyers, Lisa Limeri, Mary Lyman-Onkka, Ed McAssey, John McDonald, John McIntire, Maria Monteros, Jim Mullen, Patricio Munoz, Sandra Pelc, Anne Puzzo, Nicole Riddle, Trisha Spanbauer, Daniel Tabor, Rebecca Thomson, Kathryn Turner, Cynthia Vigueira, Kate Waselkov, Marshall Wedger, and Dan Cui Zhou. We especially thank Alexander Mahmoud (undergraduate student at Washington University in St. Louis) for processing wild accessions and maintaining them in greenhouse. We also thank Mike Dyer and other staffs of the Washington University in St. Louis greenhouse facility for providing plant care. Finally, we thank Brock Mashburn, other members of the Olsen lab group, and two anonymous reviewers for valuable comments on earlier versions of the manuscript.

W-HK was funded through the William H. Danforth Plant Science Graduate Research Fellowship in the Division of Biology and Biomedical Sciences at Washington University and by a scholarship from the Taiwan Ministry of Education. Additional funding for the project was provided by US National Science Foundation grants IOS-1557770 to KMO, and DEB-1601641 and DGE-1143954 to SJW.

Author information

Authors and affiliations.

Department of Biology, Washington University in St. Louis, St. Louis, MO, 63130, USA

Wen-Hsi Kuo, Sara J. Wright, Linda L. Small & Kenneth M. Olsen

Present address: Department of Biological and Biomedical Sciences, Rowan University, Glassboro, NJ, 08028, USA

Sara J. Wright

You can also search for this author in PubMed   Google Scholar

Contributions

W-HK designed the experiments, analyzed all data, interpreted the results, and wrote the manuscript. SJW created the mapping populations, collected and maintained the wild accessions, prepared and sequenced GBS library, and phenotyped the cyanogenesis traits. LLS maintained the plants in greenhouse, prepared GBS library, phenotyped the cyanogenesis traits, and quantified the cyanogenic glucoside content per plant. KMO conducted the greenhouse pedigree crossings, sequenced the cyanogenesis genes, conceived the project, interpreted the results, and wrote the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Kenneth M. Olsen .

Ethics declarations

Ethics approval and consent to participate.

Not applicable.

Consent for publication

Competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

12915_2024_1962_moesm1_esm.docx.

Additional file 1: Table S1. Genome assembly summary. Table S2. Total alignment length. Table S4. Genomic locations of cyanogenesis genes. Table S5. Genomic locations of Ac gene cluster. Table S6. Genomic locations of Li genes. Table S7. Accession information for cyanogenesis CNVs and resultant HCN content variation. Table S9. Primer list.

12915_2024_1962_MOESM2_ESM.xlsx

Additional file 2: Table S3. SNPs detected by direct Sanger sequencing of PCR products of Ac and Li genes in parents and F 1 progeny in greenhouse crossing experiments.

Additional file 3: Table S8. Accession information for characterizing cyanogenesis variation.

12915_2024_1962_moesm4_esm.docx.

Additional file 4: Fig. S1. GenomeScope genome size and heterozygosity assessment. Fig. S2. Phylogeny of the reverse transcriptase amino acid sequences of the Copia super-family in the white clover genome by maximum likelihood method. Fig. S3. Phylogeny of the reverse transcriptase amino acid sequences of the Gypsy super-family in the white clover genome by maximum likelihood method. Fig. S4. Chromosome categorized into T. occidentale  or T. pallescens  subgenome. Fig. S5. Coding sequence length and intron length distribution. Fig. S6. Pairwise nonsynonymous mutation rate and synonymous mutation rate between the white clover subgenomes and the diploid progenitors’ genomes. Fig. S7. Whole genome nucleotide alignment of the white clover and related species. Fig. S8. Ac gene cluster compared between white clover and Lotus japonica . Fig S9. Nucleotide alignment of the Li gene and other glucosidase genes in white clover. Fig S10. Nucleotide alignment of the cyanogenesis gene copy number variation regions between the white clover reference genome of the present study and that of Santangelo et al. Fig S11. Nucleotide alignment of the cyanogenesis gene copy number variation regions between the white clover reference genome of the present study and Wang et al. Fig. S12. Mapping result of the whole genome re-sequencing of the accessions GFL_007, DMN_010, and PI239977. Fig. S13. Pairwise gene copy number correlation of the genes in Ac gene cluster. Fig. S14. Relative gene expression and cyanide content. Fig. S15. Gene copy number variation and relative gene expression. Fig. S16. Estimated Ac and Li copy number of 419 wild accessions by genomic qPCR. Fig. S17. Association between cyanogenesis gene copy number variationand mean temperature of coldest quarter. Fig. S18. Association between cyanogenesis gene copy number variationand mean temperature of coldest quarter. Fig. S19. Whole genome nucleotide alignment between the primary haplotig and Santangelo et al. Fig. S20. Whole genome nucleotide alignment between the primary haplotig and Wang et al. Fig. S21. Nucleotide alignment and gene synteny of the white clover cyanogenesis genes with LTR-RTs labeled.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Kuo, WH., Wright, S.J., Small, L.L. et al. De novo genome assembly of white clover ( Trifolium repens L.) reveals the role of copy number variation in rapid environmental adaptation. BMC Biol 22 , 165 (2024). https://doi.org/10.1186/s12915-024-01962-6

Download citation

Received : 24 January 2024

Accepted : 24 July 2024

Published : 07 August 2024

DOI : https://doi.org/10.1186/s12915-024-01962-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Allopolyploid
• Copy number variation (CNV)
• Cyanogenesis
• Haplotype-resolved genome
• Inverted repeat-lacking clade (IRLC) legumes
• Local adaptation
• White clover ( Trifolium repens )

BMC Biology

ISSN: 1741-7007

• Submission enquiries: [email protected]
• General enquiries: [email protected]

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Published: 29 July 2024

Consumption patterns in prehistoric Europe are consistent with modern economic behaviour

• Nicola Ialongo   ORCID: orcid.org/0000-0002-3703-4808 1 , 2 &
• Giancarlo Lago   ORCID: orcid.org/0000-0001-6394-5296 3 , 4  

Nature Human Behaviour ( 2024 ) Cite this article

308 Accesses

227 Altmetric

Metrics details

• Archaeology

Have humans always sold and purchased things? This seemingly trivial question exposes one of the most conspicuous blind spots in our understanding of cultural evolution: the emergence of what we perceive today as ‘modern’ economic behaviour. Here we test the hypothesis that consumption patterns in prehistoric Europe (around 2300–800  bce ) can be explained by standard economic theory, predicting that everyday expenses are log-normally distributed and correlated to supply, demand and income. On the basis of a large database of metal objects spanning northern and southern Europe ( n  = 23,711), we identify metal fragments as money, address them as proxies of consumption and observe that, starting around 1500  bce , their mass values become log-normally distributed. We simulate two alternative scenarios and show that: (1) random behaviour cannot produce the distributions observed in the archaeological data and (2) modern economic behaviour provides the best-fitting model for prehistoric consumption.

This is a preview of subscription content, access via your institution

Access options

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

24,99 € / 30 days

cancel any time

Subscribe to this journal

Receive 12 digital issues and online access to articles

111,21 € per year

only 9,27 € per issue

Buy this article

• Purchase on Springer Link
• Instant access to full article PDF

Prices may be subject to local taxes which are calculated during checkout

Similar content being viewed by others

South American Archaeological Isotopic Database, a regional-scale multi-isotope data compendium for research

A community of practice approach to the management of metal resources, metalworking and hoarding in Bronze Age societies

Measuring frequency-dependent selection in culture

Data availability.

All data supporting the findings of this study are available within the paper and its Supplementary Information .

Code availability

Custom code that supports the findings of this study is available within the paper and its Supplementary Information and publicly available via Zenodo at https://zenodo.org/doi/10.5281/zenodo.10959515 (ref. 65 ), under license CC BY-NC 4.0 .

Mankiw, G. Principles of Microeconomics (Harcourt College Publishers, 2001).

Kristiansen, K. Towards a new paradigm? The third science revolution and its possible consequences in archaeology. Curr. Swed. Archaeol. 22 , 11–34 (2014).

Article   Google Scholar  

Sabatini, S., Earle, T. & Cardarelli, A. Bronze Age textile & wool economy: the case of the Terramare site of Montale, Italy. Proc. Prehist. Soc. 84 , 359–385 (2018).

Earle, T., Ling, J., Uhnér, C., Stos-Gale, Z. & Melheim, L. The political economy and metal trade in Bronze Age Europe: understanding regional variability in terms of comparative advantages and articulations. Eur. J. Archaeol. 18 , 633–657 (2015).

Williams, R. A. & Le Carlier de Veslud, C. Boom and bust in Bronze Age Britain: major copper production from the Great Orme mine and European trade, c. 1600–1400  bc . Antiquity 93 , 1178–1196 (2019).

Nørgaard, H. W., Pernicka, E. & Vandkilde, H. Shifting networks and mixing metals: changing metal trade routes to Scandinavia correlate with Neolithic and Bronze Age transformations. PLoS ONE 16 , e0252376 (2021).

Article   PubMed   PubMed Central   Google Scholar  

Harding, A. F. Salt in Prehistoric Europe (Sidestone Press, 2013).

Schibler, J. et al. Miners and mining in the Late Bronze Age: a multidisciplinary study from Austria. Antiquity 85 , 1259–1278 (2011).

Vandkilde, H. Bronzization: the Bronze Age as pre-modern globalization. Praehist. Z. 91 , 103–123 (2016).

Murray, S. C. Eastern Mediterranean Bronze Age trade in archaeological perspective: a review of interpretative and empirical developments. J. Archaeol. Res. 31 , 395–447 (2023).

Berger, D. et al. The Salcombe metal cargoes: new light on the provenance and circulation of tin and copper in Later Bronze Age Europe provided by trace elements and isotopes. J. Archaeol. Sci. 138 , 105543 (2022).

Article   CAS   Google Scholar  

Pare, C. in Oxford Handbook of the European Bronze Age (eds Fokkens, H. & Harding, A.) Ch. 29 (Oxford Univ. Press, 2013).

Kuijpers, M. H. G. & Popa, C. N. The origins of money: calculation of similarity indexes demonstrates the earliest development of commodity money in prehistoric Central Europe. PLoS ONE 16 , e0240462 (2021).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Ialongo, N. & Lago, G. A small change revolution: weight systems and the emergence of the first Pan-European money. J. Archaeol. Sci. 129 , e2105873118 (2021).

Ling, J., Cornell, P. & Kristiansen, K. in Modes of Production and Archaeology (eds Rosenswig, R. M. & Cunningham, J. J.) 207–230 (Univ. Press of Florida, 2017).

Kristiansen, K. in Trade and Civilisation. Economic Networks and Cultural Ties, from Prehistory to the Early Modern Era (ed. Kristiansen, K.) 87–112 (Cambridge Univ. Press, 2018).

Earle, T. & Kristiansen, K. in The Matter of Prehistory: Papers in Honor of Antonio Gilman Guillén (eds Dìaz-del-Rìo, P. et al.) 29–42 (Consejo Superior de Investigaciones Cientiìficas, 2020).

Bruck, J. in Raum, Gabe und Erinnerung: Weihgaben und Heiligtümer in prähistorischen und antiken Gesellschaften (eds Hansen, S. et al.) 75–92 (Edition Topoi, 2016).

Fontijn, D. Economies of Destruction (Routledge, 2019).

Jung, R. in The Critique of Archaeological Economy (eds Gimatzidis, S. & Jung, R.) 139–162 (Springer, 2021).

Chapman, J. Fragmentation in Archaeology: People, Places and Broken Objects in the Prehistory of South Eastern Europe (Routledge, 2000).

Finley, M. I. The Ancient Economy (Univ. California Press, 1974).

Polanyi, K. in Trade and Market in the Early Empires . Economies in History and Theory (eds Polanyi, K. et al.) 243–269 (The Free Press, 1957).

Dalton, G. Primitive money. Am. Anthropol. 67 , 44–65 (1965).

Appadurai, A. in The Social Life of Things (ed. Appadurai, A.) 3–63 (Cambridge Univ. Press, 1986).

Granovetter, M. Economic action and social structure: the problem of embeddedness. Am. J. Sociol. 91 , 481–510 (1985).

Bourdieu, P. Outline of a Theory of Practice (Cambridge Univ. Press, 1977).

Graeber, D. Debt: The First 5,000 Years (Melville House, 2011).

Blanton, R. E. & Feinman, G. M. New views on price-making markets and the capitalist impulse: beyond Polanyi. Front. Hum. Dynam. 6 , 1339903 (2024).

Barigozzi, M., Alessi, L., Capasso, M. & Fagiolo, G. The Distribution of Households Consumption–Expenditure Budget Shares . Working Paper Series 1061 (European Central Bank, 2009).

Battistin, E., Blundell, R. & Lewbel, A. Why is consumption more log normal than income? Gibrat’s Law revisited. J. Polit. Econ. 117 , 1140–1154 (2009).

Lawrence, R. J. The lognormal distribution of buying frequency rates. J. Mark. Res. 17 , 212–220 (1980).

Toda, A. A. A note on the size distribution of consumption: more Double Pareto than lognormal. Macroecon. Dynam. 21 , 1508–1518 (2017).

De Rossi, M. S. Pezzi d’Aes Rude di Peso Definito e le Asce di Bronzo Adoperate Come Valore Monetale (Tipografia Della Pace Di F. Cuggiani, 1886).

von Brunn, W. A. Die Schatzfunde der Bronzezeit als wirtschaftsgeschichtliche Quelle. Forsch. Fortsch. 21–23 , 257–260 (1947).

Peroni, R. Considerazioni ed ipotesi sul ripostiglio di Ardea. Bull. Paletnolog. Ital. 75 , 175–196 (1966).

Google Scholar  

Sperber, E. Establishing weight systems in Bronze Age Scandinavia. Antiquity 67 , 613–619 (1993).

Sommerfeld, C. Gerätegeld Sichel: Studien Zur Monetären Struktur Bronzezeitlicher Horte Im Nördlichen Mitteleuropa (De Gruyter, 1994).

Primas, M. Die Sicheln in Mitteleuropa I (C.H. Beck, 1986).

Lenerz-de Wilde, M. Prämonetäre zahlungsmittel in der kupfer und bronzezeit mitteleuropas. Fundber. Baden-Württemberg 20 , 229–327 (1995).

Peroni, R. in Mensch und Umwelt in der Bronzezeit Europas (ed. Hänsel, B.) 217–224 (Oetker-Voges Verlag, 1998).

Römisch-Germanisches Zentralmuseum Mainz. in Eliten in der Bronzezeit: Ergebnisse zweier Kolloquien in Mainz und Athen 421–514 (Römisch-Germanisches Zentralmuseum, 1999).

Cardarelli, A., Pacciarelli, M., Pallante, P. & Bellintani, P. in Pondera. Pesi e Misure nell’Antichita (eds Corti, C. & Giordani, N.) 33–58 (Museo della Bilancia, 2001).

Vilaça, R. Acerca da existencia de ponderais em contextos do Bronze Final/Ferro Inicial no território português. Arq. Port. IV , 245–288 (2003).

Ialongo, N., Hermann, R. & Rahmstorf, L. Bronze Age weight systems as a measure of market integration in Western Eurasia. Proc. Natl Acad. Sci. USA 118 , e2105873118 (2021).

Petruso, K. M. Ayia Irini. The Balance Weights: An Analysis of Weight Measurement in Prehistoric Crete and the Cycladic Islands (Philipp von Zabern, 1992).

Ialongo, N., Vacca, A. & Peyronel, L. Breaking down the bullion. The compliance of bullion-currencies with official weight-systems in a case-study from the ancient Near East. J. Archaeol. Sci. 91 , 20–32 (2018).

Rahmstorf, L. Scales, weights and weight-regulated artefacts in Middle and Late Bronze Age Britain. Antiquity 93 , 1197–1210 (2019).

Lenerz-de Wilde, M. Bronzezeitliche Zahlungsmittel. Mitteilungen der Anthropologischen Gesellschaft in Wien 132 , 1–23 (2002).

Lago, G., Cianfoni, M., Scacchetti, F., Pellegrini, L. & La Torre, A. Breaking sickles for shaping money. Testing the accuracy of weight-based fragmentation. J. Archaeolog. Sci. Rep. 49 , 103968 (2023).

Knight, M. G. Going to pieces: investigating the deliberate destruction of Late Bronze Age swords and spearheads. Proc. Prehist. Soc. 85 , 251–272 (2019).

Brandherm, D. in Gifts, Goods and Money—Comparing Currency and Circulation Systems in Past Societies (eds Brandherm, D., Heymans, E. & Hofmann, D.) 45–65 (Archaeopress, 2018).

Wiseman, R. Random accumulation and breaking: the formation of Bronze Age scrap hoards in England and Wales. J. Archaeol. Sci. 90 , 39–49 (2018).

Powell, M. A. Money in Mesopotamia. J. Econ. Soc. Hist. Orient 39 , 224–242 (1996).

Rahmstorf, L. in The Archaeology of Money (eds Haselgrove, C. & Krmnicek, S.) 19–42 (Univ. Leicester, 2016).

Dercksen, J. G. in Merchants, Measures and Money. Understanding Technologies of Early Trade in a Comparative Perspective (eds Rahmstorf, L. et al.) 331–359 (Wachholtz Verlag, 2021).

Kutscher, S. Der frühbronzezeitliche Spangenbarrenhort von Oberding, Lkr. Erding. Erste Ergebnisse. Archäologische Informationen 40 , 423–433 (2017).

von Brunn, W. A. Die Hortfunde Der Frühen Bronzezeit Aus Sachsen-Anhalt, Sachsen Und Thüringen (Akademie-Verlag, 1959).

Bianco Peroni, V. I Pugnali Nell’Italia Continentale (Franz Steiner Verlag, 1994).

Neumann, D. Landschaften Der Ritualisierung. Die Fundplätze Kupfer- Und Bronzezeitlicher Metalldeponierungen Zwischen Donau Und Po (De Gruyter, 2015).

Lago, G. Fragmentation of metal in Italian Bronze Age hoards: new instights from a quantitative analysis. Origini 44 , 171–194 (2020).

Ialongo, N. The earliest balance weights in the west: towards an independent metrology for Bronze Age Europe. Camb. Archaeol. J. 29 , 103–124 (2019).

Kendall, D. B. Hunting quanta. Philos. Trans. R. Soc. Lond. A 276 , 231–266 (1974).

Ialongo, N. & Rahmstorf, L. in Weights and Marketplaces from the Bronze Age to the Early Modern Period (eds Rahmstorf, L. & Stratford, E.) 106–126 (Wachholtz Verlag, 2019).

Lago, G. Prehistoric-consumption-supplementa-NHB: Consumption patterns in prehistoric Europe are consistent with modern economic behaviour—code. Zenodo https://doi.org/10.5281/zenodo.10959515 (2024).

Renfrew, C. in The Construction of Value in the Ancient World (eds Papadopoulos, J. K. & Urton, G.) 249–260 (Cotsen Institute of Archaeology Press, 2012).

Sutton, J. Gibrat’s legacy. J. Econ. Lit. 35 , 40–59 (1997).

Becker, G. S. & Tomes, N. An equilibrium theory of the distribution of income and intergenerational mobility. J. Polit. Econ. 87 , 1153–1189 (1979).

Hansen, S. in Materielle Kultur und Identität im Spannungsfeld zwischen mediterraner Welt und Mitteleuropa (ed. Baitinger, H.) 22–24 (Akten der Internationalen Tagung am Römisch-Germanischen Zentralmuseum Mainz, 2016).

Bradley, R. Hoards and the Deposition of Metalwork (Oxford Univ. Press, 2013).

Possen, U. M. The long-run properties of an income–expenditure model. Economica 46 , 159–173 (1979).

Akhundjanov, S. B. & Toda, A. A. Is Gibrat’s “economic inequality” lognormal? Empir. Econ. 59 , 2071–2091 (2020).

Steinkeller, P. in Commerce and Monetary Systems in the Ancient World (eds Rollinger, R. & Ulf, C.) 91–112 (Franz Steiner, 2004).

Sallaberger, W. & Pruß, A. in Labor in the Ancient World (eds Steinkeller, P. & Hudson, M.) 69–136 (ISLET-Verlag, 2015).

Otto, A. in Weights and Marketplaces from the Bronze Age to the Early Modern Period (eds Rahmstorf, L. & Stratford, E.) 203–217 (Wachholtz Verlag, 2019).

Stratford, E. A Year of Vengeance Vol. 1 (De Gruyter, 2017).

Powell, W. et al. Tin from Uluburun shipwreck shows small-scale commodity exchange fueled continental tin supply across Late Bronze Age Eurasia. Sci. Adv. 8 , eabq3766 (2022).

Knapp, A. B., Russell, A. & van Dommelen, P. Cyprus, Sardinia and Sicily: a maritime perspective on interaction, connectivity and imagination in Mediterranean prehistory. Camb. Archaeol. J. 32 , 79–97 (2022).

Dardeniz, G. & Yıldırım, T. Metal consumption of a middle-range society in the late 3rd millennium BC Anatolia: a new socioeconomic approach. PLoS ONE 17 , e0269189 (2022).

Bakker, J. D., Maurer, S., Pischke, J.-S. & Rauch, F. Of mice and merchants: connectedness and the location of economic activity in the Iron Age. Rev. Econ. Stat. 103 , 652–665 (2021).

Barjamovic, G., Chaney, T., Coşar, K. & Hortaçsu, A. Trade, merchants and the lost cities of the Bronze Age. Q. J. Econ. 134 , 1455–1503 (2019).

Benati, G., Guerriero, C. & Zaina, F. The economic and institutional determinants of trade expansion in Bronze Age Greater Mesopotamia. J. Archaeol. Sci. 131 , 105398 (2021).

Adamson, J. Trade and the rise of Ancient Greek city-states. SSRN J. https://doi.org/10.2139/ssrn.3917397 (2021).

Harding, A. European Societies in the Bronze Age (Cambridge Univ. Press, 2000).

Bruck, J. & Fokkens, H. in The Oxford Handbook of the European Bronze Age (eds Fokkens, H. & Harding, A.) (Oxford Univ. Press, 2013).

Pacciarelli, M. Dal Villaggio Alla Città. La Svolta Protourbana Del 1000 a.C. Nell’Italia Tirrenica . (All’Insegna del Giglio, 2001).

Stoddart, S. et al. Patterns of Etruscan urbanism. Front. Digit. Humanit. 7 , 1 (2020).

Kristiansen, K. & Larsson, T. B. The Rise of Bronze Age Society: Travels, Transmission and Transformations (Cambridge Univ. Press, 2005).

Galaty, M. L., Tomas, H. & Parkinson, W. A. in The Cambridge Prehistory of the Bronze and Iron Age Mediterranean (eds Knapp, A. B. & Van Dommelen, P.) 157–177 (Cambridge Univ. Press, 2015).

Vandkilde, H. Trading and weighing metals in Bronze Age Western Eurasia. Proc. Natl Acad. Sci. USA 118 , e2110552118 (2021).

Harding, A. in Oxford Handbook of the European Bronze Age (eds Fokkens, H. & Harding, A.) (Oxford Univ. Press, 2013).

Kristiansen, K. in Trade and Civilisation. Economic Networks and Cultural Ties, from Prehistory to the Early Modern Era (ed. Kristiansen, K.) 1–24 (Cambridge Univ. Press, 2018).

Angelini, I. & Bellintani, P. The use of different amber sources in Italy during the Bronze Age: new archaeometric data. Archaeol. Anthropol. Sci. 9 , 673–684 (2017).

Ling, J., Earle, T. & Kristiansen, K. Maritime mode of production: raiding and trading in seafaring chiefdoms. Curr. Anthropol. 59 , 488–524 (2018).

Bunnefeld, J.-H. et al. Baltic amber in Aššur. Forms and significance of amber exchange between Europe and the Middle East, c.2000–1300  bc . Acta Archaeol. 92 , 228–243 (2023).

Tolksdorf, J. F. et al. Evidence for Bronze Age and Medieval tin placer mining in the Erzgebirge mountains, Saxony (Germany). Geoarchaeology 35 , 198–216 (2020).

Niederschlag, E., Pernicka, E., Seifert, T. & Bartelheim, M. The determination of lead isotope ratios by multiple collector Icp‐Ms: a case study of Early Bronze Age artefacts and their possible relation with ore deposits of the Erzgebirge. Archaeometry 45 , 61–100 (2003).

Pernicka, E., Lutz, J. & Stöllner, T. Bronze Age copper produced at Mitterberg, Austria and its distribution. Archaeol. Austriaca 100 , 19–55 (2016).

Gavranović, M. et al. Emergence of monopoly—copper exchange networks during the Late Bronze Age in the western and central Balkans. PLoS ONE 17 , e0263823 (2022).

Chiarantini, L. et al. Copper metallurgy in ancient Etruria (southern Tuscany, Italy) at the Bronze–Iron Age transition: a lead isotope provenance study. J. Archaeol. Sci. Rep. 19 , 11–23 (2018).

Jones, R., Levi, S. T., Bettelli, M. & Vagnetti, L. Italo-Mycenaean Pottery: The Archaeological and Archaeometric Dimensions (CNR—Istituto di Studi sul Mediterraneo Antico, 2014).

Tarbay, J. G. Twin Hoards. Metals and Deposition in the Buda Hills, the Pilis and the Visegrád Mountains during the Late Bronze Age (Hungarian National Museum, 2022).

Lago, G. La Frammentazione dei Metalli nei Ripostigli Europei dell’Età del Bronzo (‘Sapienza’ Univ. Rome, 2022).

Müller Karpe, H. Beiträge zur Chronologie der Urnenfelderzeit Nördlich und Südlich der Alpen (Walter de Gruyter, 1959).

Reinecke, P. Zur chronologischen Gliederung der süddeutschen Bronzezeit. Germania 8 , 43–44 (1924).

Brunner, M., Von Felten, J., Hinz, M. & Hafner, A. Central European Early Bronze Age chronology revisited: a Bayesian examination of large-scale radiocarbon dating. PLoS ONE 15 , e0243719 (2020).

Müller, J. & Lohrke, B. Neue absolutchronologische daten für die süddeutsche Hügelgräberbronzezeit. Germania 87 , 25–39 (2009).

Sperber, L. Studien zur Spätbronzezeitlichen Chronologie im Westlichen Mitteleuropa und in Westeuropa (Schnell & Steiner Verlag, 2017).

Montelius, O. Om Tidsbestämning Inom Bronsåldern Med Sårskilt Avseende På Skandinavien (Kongl. Boktryckeriet, 1885).

Vandkilde, H., Rahbek, U. & Rasmussen, K. L. Radiocarbon dating and the chronology of Bronze Age southern Scandinavia. Acta Archaeol. 67 , 183–198 (1996).

Olsen, J., Margrethe Hornstrup, K., Heinemeier, J., Bennike, P. & Thrane, H. Chronology of the Danish Bronze Age based on 14 C dating of cremated bone remains. Radiocarbon 53 , 261–275 (2011).

Turk, P. in Hoards and Individual Metal Finds From the Eneolithic and Bronze Ages in Slovenia (ed. Teržan, B.) 89–124 (Narodni Muzej, 1995).

Carancini, G. L. & Peroni, R. L’età Del Bronzo in Italia: Per Una Cronologia Della Produzione Metallurgica (Università di Perugia, 1999).

Cardarelli, A. Before the city: the last villages and proto-urban centres between the Po and Tiber rivers. Origini 42 , 359–382 (2018).

Pakkanen, J. in Tracing Prehistoric Social Networks Through Technology: A Diachronic Perspective on the Aegean (ed. Brysbaert, A.) 143–166 (Routledge, 2011).

Cohen, J. ‘The Earth is Round ( p  < .05)’. Am. Psychol. 49 , 997–1003 (1994).

Cohen, J. Statistical Power Analysis for the Behavioral Sciences (Psychology Press, 2009).

Sullivan, G. M. & Feinn, R. Using effect size—or why the P value is not enough. J. Graduate Med. Educ. 4 , 279–282 (2012).

Lago, G. Prehistoric-consumption-supplementa-NHB. Zenodo https://doi.org/10.5281/zenodo.10959516 (2024).

Download references

Acknowledgements

We wish to thank Eleonore Pape, Elise Pape, N. Pape and D. Berger for their comments and advice on earlier versions of this work. The authors received no specific funding for this work.

Author information

Authors and affiliations.

Seminar für Ur- und Frühgeschichte, Georg-August-Universität Göttingen, Göttingen, Germany

Nicola Ialongo

Department of Archeology and Heritage Studies, Aarhus University, Aarhus, Denmark

Dipartimento di Beni Culturali, Università del Salento, Lecce, Italy

Giancarlo Lago

Department of Economics, University of Bologna, Bologna, Italy

You can also search for this author in PubMed   Google Scholar

Contributions

N.I. designed the research, performed data analysis and wrote the article. G.L. performed data analysis, wrote the article and created the code.

Corresponding author

Correspondence to Nicola Ialongo .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature Human Behaviour thanks Maciej Kasiński and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended data fig. 1 orders of magnitude..

The boxplots show the distribution of the mass values balance weights pertaining to the shekel- and the mina- range, compared to the distribution of metal fragments and complete objects. Dotted lines indicate the approximate value of the weight units. Solid lines indicate the boundaries of the CQA sampling (7-200 g). Shekel : n = 244, min.=0.16 g, max.=469.41 g, centre=9,.77 g, box bottom=4.08 g, box top=32.01 g, whisker bottom=0.16 g, whisker top=100.00 g. Fragments: n = 6,881, min.=0.01 g, max.=9,220 g, centre=26.00 g, box bottom=7.90 g, box top=82.00 g, whisker bottom=2.70 g, whisker top=233.00 g. Complete: n = 6,746, min.=0.27 g, max.=8,750 g, centre=181.00 g, box bottom=375.27 g, box top=210.00 g, whisker bottom=31.30 g, whisker top=340.00 g. Mina : n = 367, min.=11.8 g, max.=5,050 g, centre=592.00 g, box bottom=375.27 g, box top=908.30 g, whisker bottom=235.20 g, whisker top=998.36 g. Outliers method: 1.5*Interquartile range. Graph made with Wavemetrics Igor Pro 6.05.0.

Extended Data Fig. 2 CQA, diachronic results.

Dotted lines represent the raw output of the quantal analysis. Solid lines represent the same output after smoothing. We indicate the number of measurements sub-sampled in each quantogram, and in parentheses the total sample size.

Extended Data Fig. 3 Monte Carlo Simulation.

CQA of the sample of bronze fragments of Phases 2-3 compared to the CQA results for balance weights. Left Y axis: balance weights. Right Y axis: bronze fragments. The horizontal dashed line represents the 5% significance level for the Monte Carlo simulation ( ϕ (q)=7.35), lower than the peak value of bronze fragments ( ϕ (q)=7.77).

Extended Data Fig. 4 Binned Frequency Distribution Analysis of metal fragments.

Bin size=1.11 g. The dots overlaid on the histograms represent multiples of 10 g (that is, the approximate value of the Pan-European shekel ); the boundaries represent a CV = 5% at 1 SD. The dots’ Y values are arbitrarily placed for visual clarity. Above: Phase 1 (n = 397). Below: Phase 2 (n = 3,339) and 3 (n = 3,145).

Extended Data Fig. 5 Binned Frequency Distribution Analysis of complete metal objects.

Bin size=11.1 g. The dots overlaid on the histograms represent multiples of the theoretical value of Pan-European shekel of 10 g; the whiskers represent CV = 5% of each of these multiples at 1 SD. The dots’ Y values are arbitrarily offset, in order to avoid visual confusion generated by the overlapping error margins. Above: Phase 1 (n = 4,558). Below: Phase 2 (n = 1,075) and 3 (n = 1,113).

Extended Data Fig. 6 One-sample Kolmogorov Smirnov test for normality (two-sided).

The graphs compare the Cumulative Distribution Functions (CDF) of archaeological datasets and normal distributions. The p -values and test statistic D of each test are shown on the graphs.

Extended Data Fig. 7 Simulation scenario 2: Monetary fragmentation.

Green area: CI = 95% of the simulated results (DDF and Q-Q plots). Green line: mean values of the simulated results (DDF and Q-Q plots). Orange lines: KDE of the distribution of metal fragments. Orange dots: Q-Q plots of the distribution of metal fragments.

Supplementary information

Reporting summary, peer review file, supplementary data 1.

Database of the metal objects from Bronze Age hoards considered in this study.

Supplementary Data 2

Database of Bronze Age balance weights.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Cite this article.

Ialongo, N., Lago, G. Consumption patterns in prehistoric Europe are consistent with modern economic behaviour. Nat Hum Behav (2024). https://doi.org/10.1038/s41562-024-01926-4

Download citation

Received : 21 August 2023

Accepted : 13 June 2024

Published : 29 July 2024

DOI : https://doi.org/10.1038/s41562-024-01926-4

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

HYPOTHESIS AND THEORY article

From diagnosis to dialogue – reconsidering the dsm as a conversation piece in mental health care: a hypothesis and theory.

• 1 Department of Psychiatry, Utrecht University Medical Center, Utrecht, Netherlands
• 2 Digital Innovation in Health, NHL Stenden University of Applied Sciences, Leeuwarden, Netherlands
• 3 Department of Research and Innovation, KieN VIP Mental Health Care Services, Leeuwarden, Netherlands
• 4 Department of Child and Family Welfare, University of Groningen, Groningen, Netherlands

The Diagnostic and Statistical Manual of Mental Disorders, abbreviated as the DSM, is one of mental health care’s most commonly used classification systems. While the DSM has been successful in establishing a shared language for researching and communicating about mental distress, it has its limitations as an empirical compass. In the transformation of mental health care towards a system that is centered around shared decision-making, person-centered care, and personal recovery, the DSM is problematic as it promotes the disengagement of people with mental distress and is primarily a tool developed for professionals to communicate about patients instead of with patients. However, the mental health care system is set up in such a way that we cannot do without the DSM for the time being. In this paper, we aimed to describe the position and role the DSM may have in a mental health care system that is evolving from a medical paradigm to a more self-contained profession in which there is increased accommodation of other perspectives. First, our analysis highlights the DSM’s potential as a boundary object in clinical practice, that could support a shared language between patients and professionals. Using the DSM as a conversation piece, a language accommodating diverse perspectives can be co-created. Second, we delve into why people with lived experience should be involved in co-designing spectra of distress. We propose an iterative design and test approach for designing DSM spectra of distress in co-creation with people with lived experience to prevent the development of ‘average solutions’ for ‘ordinary people’. We conclude that transforming mental health care by reconsidering the DSM as a boundary object and conversation piece between activity systems could be a step in the right direction, shifting the power balance towards shared ownership in a participation era that fosters dialogue instead of diagnosis.

1 Introduction

The Diagnostic Statistical Manual of Mental Disorders (DSM) has great authority in practice. The manual, released by the American Psychiatric Association (APA), provides a common language and a classification system for clinicians to communicate about people’s experiences of mental distress and for researchers to study social phenomena that include mental distress and its subsequent treatments. Before the DSM was developed, a plethora of mental health-related documents circulated in the United States ( 1 ). In response to the confusion that arose from this diversity of documents, the APA Committee on Nomenclature and Statistics standardized these into one manual, the DSM-I ( 2 ). In this first edition of the manual, released in 1952, mental distress was understood as a reaction to stress caused by psychological and interpersonal factors in the person’s life ( 3 ). Although the DSM-I had limited impact on practice ( 4 ), it did set the stage for increasingly standardized categorization of mental disorders ( 5 ).

The DSM-II was released in 1968. In this second iteration, mental disorders were understood as the patient’s attempts to control overwhelming anxiety with unconscious, intrapsychic conflicts ( 3 ). In this edition, the developers attempted to describe the symptoms of disorders and define their etiologies. They had chosen to base them predominantly on psychodynamic psychiatry but also included the biological focus of Kraepelin’s system of classification ( 5 , 6 ). During the development of the DSM-III, the task force added the goal to improve the reliability — the likelihood that different professionals arrive at the same diagnosis — of psychiatric diagnosis, which now became an important feature of the design process. The developers abandoned the psychodynamic view and shifted the focus to atheoretical descriptions, aiming to specify objective criteria for diagnosing mental disorders ( 3 ). Although it was explicitly stated in DSM-III that there was no underlying assumption that the categories were validated entities ( 7 ), the categorical approach still assumed each pattern of symptoms in a category reflected an underlying pathology. The definition of ‘mental illness’ was thereby altered from what one did or was (“you react anxious/you are anxious”) to something one had (“you have anxiety”). This resulted in descriptive, criteria-based classifications that reflected a perceived need for standardization of psychiatric diagnoses ( 5 , 6 ). The DSM-III was released in 1980 and had a big impact on practice ( 6 ) as it inaugurated an attempt to “re-medicalize” American psychiatry ( 5 ).

In hindsight, it is not surprising that after the release of the DSM-III, the funding for psychopharmacological research skyrocketed ( 8 ). At the same time, the debate on the relationship between etiology and description in psychiatric diagnosis continued ( 9 ). As sociologist Andrew Scull ( 10 ) showed, the election of President Reagan prompted a shift towards a focus on biology. His successor, President Bush, claimed that the 1990s were ‘the decade of the brain,’ which fueled a sharp increase in funding for research on genetics and neuroscience ( 10 ). Despite the public push for biological research, the DSM-IV aimed to arrive at a purely atheoretical description of psychiatric diagnostic criteria and was released in 1994 ( 11 ). The task force conducted multi-center field trials to relate diagnoses to clinical practice to improve reliability, which remained a goal of the design process ( 12 ). While the DSM-IV aimed to be atheoretical, researchers argued that the underlying ontologies were easily deducible from their content: psychological and social causality were eliminated and replaced implicitly with biological causality ( 13 ). In the DSM-5, validity — whether a coherent syndrome is being measured and whether it is what it is assumed to be — took center stage ( 10 ). The definition of mental disorder in the DSM-5 was thereby conceptualized as:

“… a syndrome characterized by clinically significant disturbance in an individual’s cognition, emotion regulation, or behavior that reflects a dysfunction in the psychological, biological, or developmental processes underlying mental functioning.” ( 14 ).

With the release of the DSM-5, the debate surrounding the conceptualization of mental distress started all over again, but this can be best seen as re-energizing longstanding debates around the utility and validity of APA nosology ( 15 ). Three important design goals from the DSM-III until current editions can be observed: providing an international language on mental distress, developing a reliable classification system, and creating a valid classification system.

1.1 The limitations of the DSM as an empirical compass

The extent to which these three design goals were attained is only partial. The development of an international language has been accomplished, as the DSM (as well as the International Classification of Diseases) is now widely employed across most Western countries. Although merely based on consensus, the DSM enables — to an extent — professionals and researchers to quantify the prevalence of certain behaviors and find one or more classifications that best suit these observed behaviors. To this date, the expectation that diagnostic criteria would be empirically validated through research has not yet been fulfilled ( 10 , 16 , 17 ). As stated by the authors of the fourth edition ( 11 ), the disorders listed in the DSM are “valuable heuristic constructs” that serve a purpose in research and practice. However, it was already emphasized in the DSM-IV guidebook that they do not precisely depict nature as it is, being characterized as not “well-defined entities” ( 18 ). Furthermore, while the fifth edition refers to “syndromes,” it is again described that “there is no assumption that each category of mental disorder is a completely discrete entity with absolute boundaries dividing it from other mental disorders or from no mental disorder” ( 14 ). Consequently, there are no laboratory tests or biological markers to set the boundary between ‘normal’ and ‘pathological,’ thus, it cannot confirm or reject the presumed pathologies underlying the DSM classifications, thereby rendering the validity goal of the design unattained. Therefore, the reliability of the current major DSM (i.e., DSM-5) still raises concerns ( 19 ).

By focusing conceptually on mental distress as an individual experience, the DSM task forces have neglected the role of social context, potentially restricting a comprehensive clinical understanding of mental distress ( 20 ). There is mounting evidence and increased attention, however, that the social environment, including its determinants and factors, is crucial for the onset, course, and outcome of mental distress ( 21 – 27 ). Moreover, exposure to factors such as early life adversity, poverty, unemployment, trauma, and minority group position is strongly associated with the onset of mental distress ( 28 , 29 ). It is also established that the range of ontological perspectives — what mental distress is and how it exists — is far broader than what is typically covered in prevailing scientific and educational discussions ( 30 ). These diverse perspectives are also evident in the epistemic pluralism among theoretical models on mental health problems ( 31 ).

1.2 The DSM is problematic in the transformation of mental health care

In the context of contemporary transformations in mental health care, the role of the DSM as an empirical instrument becomes even more problematic. In recent years, significant shifts have been witnessed in mental health care services, with a growing focus on promoting mental well-being, preventive measures, and person-centered and rights-based approaches ( 32 ). In contrast to the 1950s definition of health in which health was seen as the absence of disease, health today is defined as “the ability to adapt and to self-manage” ( 33 ), also known as ‘positive health.’ Furthermore, the recovery movement ( 34 ), person-centered care ( 35 ), and the integration of professionals’ lived experiences ( 36 ) all contributed to a more person-centered mental health care that promotes shared-decision making as a fundamental principle in practice in which no one perspective holds the wisdom. Shared decision-making is “an approach where clinicians and patients share the best available evidence when faced with the task of making decisions, and where patients are supported to consider options, to achieve informed preferences” ( 37 ). To realize and enable a more balanced relationship between professional and patient in shared decision-making, the interplay of healthcare professionals’ and patients’ skills, the support for a patient, and a good relationship between professional and patient are important to facilitate patients’ autonomy ( 38 ). Thus, mental health care professionals in the 21st century should collaborate, embrace ideography, and maximize effects mediated by therapeutic relationships and the healing effects of ritualized care interactions ( 39 ).

The DSM and its designed classifications, as well as their use in the community, can hinder a person-centered approach in which meaning is collaboratively derived for mental health issues, where a balanced relationship is needed, and where decisions are made together. We can demonstrate this with a brief example involving the ADHD classification and its criteria, highlighting how its design tends to marginalize individuals with mental distress, reducing their behavior to objectification from the clinician’s viewpoint. The ADHD classification delineates an ideal self that highly esteems disengagement from one’s feelings and needs, irrespective of contextual factors ( 40 ). This inclination is apparent in the criteria, including criterion 1a concerning inattention: “often avoids, dislikes, or is reluctant to engage in tasks that require sustained mental effort”. This indicates that disliking something is viewed as a symptom rather than a personal preference ( 40 ). Due to a lack of attention to the person’s meaning, a behavior that may be a preference of the individual can become a symptom of a disease. Another instance can be observed in criterion 2c: “often runs about or climbs in situations where it is inappropriate.” Although such behavior might be deemed inappropriate in certain contexts, many individuals derive enjoyment from running and climbing. In this way, ‘normal’ human behavior can be pathologized because there is no room for the meaning of the individual.

A parallel disengagement is evident in the DSM’s viewpoint on individuals with mental distress ( 40 ), as the diagnostic process appears to necessitate no interaction with an individual; instead, it fosters disengagement rather than engagement. For example, according to the DSM-5, when a child is “engaged in especially interesting activities,” the clinician is warned that the ‘symptoms’ may not manifest. Although it appears most fitting to assist the child by exploring their interests, clinicians are instead encouraged to seek situations the child finds uninteresting and assess whether the child can concentrate ( 40 ). If the child cannot concentrate, a ‘diagnosis’ might be made, and intervention can be initiated. This highlights that the design of the DSM promotes professionals to locate individual disorders in a person at face value without considering contextual factors, personal preferences, or other idiosyncrasies in a person’s present or history ( 41 ). It is also apparent that the term ‘symptom’ in the DSM implies an underlying entity as its cause, obscuring that it is a subjective criterion based on human assessment and interpretation ( 42 ). These factors make it difficult for the DSM in its current form to have a place in person-centered mental health care that promotes shared decision-making.

1.3 The problem and hypotheses

Diagnostic manuals like the DSM function similarly to standard operating procedures: they streamline decision-making and assist professionals in making approximate diagnoses when valid and specific measures are lacking or not readily accessible ( 43 ). However, the DSM is often (mis)used as a manual providing explanations for mental distress. This hinders a personalized approach that prioritizes the patient’s needs. Furthermore, this approach does not align with the principles of shared decision-making, as the best available evidence indicates that classifications are not explanations for mental distress. Also, disengagement is promoted in the design of the DSM, which is problematic in the person-centered transformation of mental health care in which a range of perspectives and human-centered interventions are needed. This paper aims to describe the position and role the DSM may have in a mental health care system that is evolving from a medical paradigm to a more self-contained profession in which there is increased accommodation of other perspectives. For this hypothesis and theory paper, we have formulated the following hypotheses:

(1) Reconsidering the DSM as a boundary object that can be used as a conversation piece allows for other perspectives on what is known about mental distress and aligns with the requirements of person-centered mental health care needed for shared decision-making;.

(2) Embracing design approaches in redesigning the DSM to a conversation piece that uses spectra of mental distress instead of classifications will stimulate the integration of diverse perspectives and voices in reshaping mental health care.

2 Co-creation of a real common language

The DSM originally aimed to develop a common language, and it has achieved that to some extent, but it now primarily serves as a common language among professionals. This does not align with the person-centered transformation in mental health care, where multiple perspectives come into play ( 32 , 44 ). In this section, we will address our first hypothesis: reconsidering the DSM as a boundary object that can be used as a conversation piece allows for other perspectives on what is known about mental distress and aligns with the requirements of person-centered mental health care needed for shared decision-making. First, we will examine several unintended consequences of classifications. After that, we propose considering the DSM as boundary objects to arrive at a real common language in which the perspective of people with lived experience is promoted. This perspective views the DSM as a conversation piece that can be used as a subject, the meaning of which can be attributed from various perspectives where the premise is that there is not an omniscient perspective.

2.1 Validation, stigma, and making up people

Classifications influence what we see or do not see, what is valorized, and what is silenced ( 45 ). DSM classifications and the process of getting them can provide validation and relief for some service users, while for others, it can be stigmatizing and distressing ( 46 , 47 ). The stigma people encounter can be worse than the mental problems themselves ( 48 ). The classification of people’s behaviors is not simply a passive reflection of pre-existing characteristics but is influenced by social and cultural factors. The evolution of neurasthenia serves as a fascinating illustration of the notable ontological changes in the design of the DSM, constantly reflecting and constructing reality. Initially, neurasthenia was considered a widespread mental disorder with presumed somatic roots. Still, it was subsequently discarded from use, only to resurface several decades later as a culture-bound manifestation of individual mental distress ( 49 ). Consequently, certain mental disorders, as depicted in the DSM, may not have existed in the same way as before the classifications were designed. This has been called ‘making up people’, which entails the argument that different kinds of human beings and human acts come into being hand in hand with our invention of the categories labeling them ( 50 ). Furthermore, it is important to consider that whether behavior is deemed dysfunctional or functional is always influenced by the prevailing norms and traditions within a specific society at a given time. Therefore, the individual meaning of the patient in its context is always more important than general descriptions and criteria of functional and dysfunctional behavior (i.e., ADHD climbing example).

Individuals might perceive themselves differently and develop emotions and behaviors partly due to the classifications imposed upon them. Over time, this can result in alterations to the classification itself, a phenomenon referred to as the classificatory looping effect ( 51 ). Moreover, when alterations are made to the world that align with the system’s depiction of reality, ‘blindness’ can occur ( 45 ). To illustrate, let’s consider an altered scenario of Bowker and Star ( 45 ) in which all mental distress is categorized solely based on physiological factors. In this context, medical frameworks for observation and treatment are designed to recognize physical manifestations of distress, such as symptoms, and the available treatments are limited to physical interventions, such as psychotropic medications. Consequently, in such a design, mental distress may solely be a consequence of a chemical imbalance in the brain, making it nearly inconceivable to consider alternative conceptualizations or solutions. Thus, task forces responsible for designing mental disorder classifications should be acutely aware that they actively contribute to the co-creation of reality with the classifications they construct upon reality ( 49 ).

2.2 Reification and disorderism

Another unintended consequence is the reification of classifications. Reification involves turning a broad and potentially diverse range of human experiences into a fixed and well-defined category. Take, for example, the case of the classification of ADHD and its reification mechanisms (i.e., language choice, logical fallacies, genetic reductionism, and textual silence) ( 42 ). Teachers sometimes promote the classification of ADHD as they believe it acknowledges a prior feeling that something is the matter with a pupil. The classification is then seen as a plausible explanation for the emergence of specific behaviors, academic underperformance, or deviations from the expected norm within a peer group ( 52 , 53 ). At first glance, this may seem harmless. However, it reinforces the notion that a complex and multifaceted set of contextual behaviors, experiences, and psychological phenomena are instead a discrete, objective entity residing in the individual. This is associated with presuppositions in the DSM that are not explicitly articulated, such as attributing a mental disorder to the individual rather than the system, resulting in healthcare that is organized around the individual instead of organized around the system ( 54 ).

In this way, DSM classifications can decontextualize mental distress, leading to ‘disorderism’. Disorderism is defined as the systemic decontextualization of mental distress by framing it in terms of individual disorders ( 55 ). The processes by which people are increasingly diagnosed and treated as having distinct treatable individual disorders, exemplified by the overdiagnosis of ADHD in children and adolescents ( 56 ), while at the same time, the services of psychiatry shape more areas of life, has been called the ‘psychiatrization of society’ ( 57 ). The psychiatrization of society encompasses a pervasive influence whereby the reification and disorderism extend beyond clinical settings and infiltrate various facets of daily life. It is a double-edged sword that fosters increased awareness of mental health issues and seeks to reduce stigma, but at the same time, raises concerns about the overemphasis on medical models, potentially neglecting the broader social, cultural, and environmental factors that contribute to individual well-being as well as population salutogenesis ( 58 ).

2.3 The DSM as a boundary object between activity systems in clinical practice

Instead of using the DSM as a scientific and professional tool in order to classify, the DSM can be reconsidered as a boundary object. When stakeholders with different objectives and needs have to work together constructively without making concessions, like patients and professionals in person-centered mental health care, objects can play a bridging role. Star and Griesemer ( 59 ) introduced the term boundary objects for this purpose.

“Boundary objects are objects that are plastic enough to adapt to the local needs and constraints of the different parties using them, yet robust enough to maintain a common identity in different locations. They are weakly structured in common use and become strongly structured in use in individual locations. They can be abstract or concrete. They have different meanings in different social worlds, but their structure is common enough to more than one world to make them recognizable, a means of translation.” ( 59 ).

Before exploring the benefits of a boundary object perspective for the DSM, it is important to note that it remains questionable whether the DSM in its current form can help establish a shared understanding or provide diagnostic, prognostic, or therapeutic value ( 60 – 63 ). To make the DSM more suitable for accommodating different perspectives and types of knowledge, the DSM task force can focus its redesign on leaving the discrete disease entities — which classifications imply — behind by creating spectra. This way of thinking has already found its way to the DSM-5, in which mental distress as a spectrum was introduced in the areas of autism, substance use, and nearly personality disorders, and following these reconceptualization, also a psychosis spectrum was proposed ( 43 ), but this proposition was eventually not adopted in the manual. As mental distress can be caused by an extensive range of factors and mechanisms that result from interactions in networks of behaviors and patterns that have complex dynamics that unfold over time ( 64 ), spectra of mental distress may be more suitable for conversations about an individual’s narrative and needs in clinical practice, as each experience of mental distress is unique and contextual.

If the DSM is reconsidered as a boundary object that is intended to provide a shared language for interpreting mental distress while addressing the unintended consequences of classifications, it is also essential to consider where this language now primarily manifests itself, how it relates to shared decision-making, and the significant role it plays for patients in the treatment process. In recent decades, the DSM has positioned itself primarily as a professional tool for clinical judgment (see Figure 1 ). In this way, professionals have more or less acquired a monopoly on the language of classifications and the associated behaviors and complaints described in the DSM. It provides professionals with a tool to pursue their professional objectives and legitimacy for their professional steps with patients, resulting in a lack of equality from which different perspectives can be examined side by side. However, with shared decision-making, patients are expected to be engaged and to help determine the course of treatment; the language surrounding classifications and symptoms does not currently allow that to happen sufficiently.

Figure 1 DSM as a professional tool, adapted from Figure 1, ‘Design of a Digital Comic Creator (It’s Me) to Facilitate Social Skills Training for Children With Autism Spectrum Disorder: Design Research Approach’, by Terlouw et al., CC-BY ( 65 ).

This is where boundary objects come into play. The focused shaping of boundary objects can ensure a more equal role for different stakeholders ( 65 – 67 ). Boundary objects can also trigger perspective-making and -taking from a reflective dialogical learning mechanism ( 68 – 70 ), which ensures a better shared understanding of all perspectives. Boundary objects and their dialogical learning mechanisms also align well with co-design ( 71 ). If we consider the DSM a boundary object, it positions itself between the activity system of the professionals, patients, and other people close to the patient ( Figure 2 ). The boundary between activity systems represents not only the cultural differences and potential challenges in actions and interactions but also the significant value in establishing communication and collaboration ( 71 ). All sides can give meaning to the DSM language from their perspective. By effectively considering the DSM as a boundary object, the DSM serves as a conversation piece—a product that elicits and provides room for questions and comments from other people, literally one that encourages conversation ( 72 ). As a conversation piece rather than a determinative classification system, it can contribute to mapping the meaning of complaints, behaviors, signs, and patterns for different invested parties. It also provides space for the patient’s contextual factors, subjective experience, needs, and life events, which are essential to giving constructive meaning to mental distress. This allows for interpretative flexibility; professionals can structure their work, while patients can give meaning to their subjective experience of mental distress.

Figure 2 DSM as a boundary object, adapted from Figure 1, ‘Design of a Digital Comic Creator (It’s Me) to Facilitate Social Skills Training for Children With Autism Spectrum Disorder: Design Research Approach’, by Terlouw et al., CC-BY ( 65 ).

As the DSM as a boundary object enables interpretative flexibility, it could then be used to enact conversations and develop a shared understanding in partnership between the patient and the professional; patients are no longer ‘diagnosed’ with a disorder from a professional point of view. It is important to note that the conceptual history of understanding the diagnostic process as essentially dialogical and not as a merely technical-quantitative procedure was already started in the early 1900s. For example, in the 1913 released ‘General Psychopathology,’ Karl Jaspers presented a phenomenological and comprehensive perspective for psychiatry with suggestions about how to understand the psychopathological phenomena as experienced by the patient through empathic understanding, allowing to understand the patient’s worldview and existential meanings ( 73 ). A century after its first publication, academics continue to leverage Jaspers’ ideas to critique modern operationalist epistemology ( 74 ). Following the notion of the diagnostic process as a dialogical one, the reconsideration of the DSM as a boundary object could accommodate the patient’s idiographic experience and the professional’s knowledge about mental distress by using these potential spectra as conversation pieces, shifting the power balance in clinical practice towards co-creation and dialogue. The spectra can then be explained as umbrella terms that indicate a collection of frequently occurring patterns and signs that can function as a starting point for a co-creative inquiry that promotes dialogue, aligning more with current empirical evidence of lived experience than using classifications as diagnoses.

Considering the advantages and strengths boundary objects bring to a mental health care system centered around shared decision-making and co-creation, the DSM could be a boundary object that is interpreted from various perspectives. Take, for example, altered perceptions, which is a characteristic commonly seen in people who receive a psychosis-related classification in clinical care. For some, these perceptions have person-specific meaning ( 75 , 76 ). By using the DSM as a boundary object and as a conversation piece, the patient and professional can give meaning by using the spectra in the manual as a starting point for a common language instead of using a classification to explain the distress. This requires a phenomenological and idiographic approach considering person-specific meaning and idiosyncrasies. Consequently, diagnostic practices should be iterative to align with the dynamic circumstances, with the individual’s narrative taking center stage in co-creation between professional and patient ( 41 , 49 ), as this reconsidered role fosters the engagement instead of the disengagement of patients. Additionally, the potential role of the DSM as a boundary object and conversation piece may also have a positive effect on societal and scientific levels, specifically on how mental distress is perceived and conceptualized. It can ‘systemically contextualize’ mental distress, which could eliminate the disorderism and the psychiatrization of society, and in the end, hopefully, contribute to population salutogenesis.

3 Co-design of DSM spectra of mental distress

If the DSM is reconsidered as a conversation piece in which spectra of mental distress replace classifications, it is important to address that these must be co-designed to accommodate diverse stakeholder perspectives and various types of knowledge side by side in clinical practice. Therefore, developers and designers need to embrace lived experience in the co-development of these spectra of mental distress to ensure patients’ engagement in clinical practice, as the patient effectively becomes a stakeholder of the DSM. This requires a different approach and procedure than DSM task forces used in past iterations. In this section, we will address our second hypothesis: embracing design approaches in redesigning the DSM to a conversation piece that uses spectra of mental distress instead of classifications will stimulate the integration of diverse perspectives and voices in reshaping mental health care. While we focus a little on the what (spectra of mental distress), we mainly focus on the how (the procedure that could be followed to arrive at the what). First, we will discuss the importance of lived experience leadership in design and research. Second, we argue that in the conceptual co-design of DSM spectra, lived experience leadership can be a way forward. Third, we take the stance that a designerly way of thinking and doing can shift the premature overcommitment task forces had to iterative exploration. In the concluding paragraph, we propose a design procedure that embraces engagement and iteration as core values for developing robust and flexible spectra of mental distress that are meaningful for service users and professionals.

3.1 Lived experience leadership and initiatives in design and research

First, let us briefly examine the evolution of lived experience in design and science over time to provide context for why engaging people with lived experience in the design of spectra of mental distress is important for innovation. Since 1960, people with lived experiences have tried to let their voices be heard, but initially to no avail, and their civil rights movement of reformist psychiatry was labeled as ‘anti-psychiatry’ ( 77 ). During the turn of the millennium, lived experience received increased recognition and eventually became an important pillar of knowledge that informed practice and continues to do so on various levels of mental health care ( 34 , 36 , 78 – 81 ). While there is currently growing attention to the perspective of lived experience in, for example, mental health research ( 79 , 80 , 82 , 83 ) and mental health care design and innovation ( 84 – 90 ), overall, their involvement remains too low in the majority of research and design projects ( 88 , 91 , 92 ). While there has been a significant increase in the annual publication of articles claiming to employ collaborative methods with people with lived experience, these studies often use vague terms to suggest a higher engagement level than is the case ( 93 ). This has led to initiatives such as that of The Lancet Psychiatry to facilitate transparent reporting of lived experience work ( 93 , 94 ).

Although the involvement of people with lived experience and its reporting needs attention in order to prevent tokenism and co-optation ( 89 ), some great user-driven initiatives resulted in innovative design and research that improved mental health care and exemplifies why their engagement should be mandatory. The Co-Design Living Labs is such an initiative. Its program exemplifies an adaptive and embedded approach for people with lived experiences of mental distress to drive mental health research design to translation ( 95 ). In this community-based approach, people with lived experience, their caregivers, family members, and support networks collaboratively drive research with university researchers, which is very innovative considering the relatively low engagement of people with lived experience in general mental health research. Another example is the development of person-specific tapering medication initiated by people with lived experience of withdrawal symptoms. People with lived experience began to devise practical methods to discontinue medications on their own safely because of the lack of a systematic and professional response to severe and persistent withdrawal. This resulted in the accumulation of experience-based knowledge about withdrawal, ultimately leading to co-creating what is now known as tapering strips ( 81 ). The development of these tapering strips shows that people with lived experience have novel experience-based ideas for design and research that can result in human-centered innovation. Both examples underline the importance of human-centered design in which people with lived experience and knowledge are taken seriously and why the participation era requires that individuals with lived experience are decision-makers from the project’s start to produce novel perspectives for innovative design and research ( 88 , 93 ).

3.2 The conceptual co-design of DSM spectra of mental distress and the potential of integrating lived experiences

Engaging people with lived experience of mental distress in redesigning the DSM towards a spectrum-based guideline is of special importance, albeit a more conceptual design task in comparison to the earlier examples. What mental distress is remains a fundamental philosophical and ontological question that should be addressed in partnership as it sits at the core of how mental health care is organized. To allow novel ontologies to reach their full potential and act as drivers of a landscape of promising innovative scientific and clinical approaches, investment is required in development and elaboration ( 30 ). This, as well as the epistemic pluralism among theoretical models on mental health problems ( 31 ), makes it evident there is currently not one coherent accepted explanation or consensus on what mental distress is and how it exists. Without clear etiological understanding, the most logical first step should be to involve people with lived experience of mental distress in the redevelopment of the DSM. Accounts from people with lived experience of mental distress are directly relevant to the design of the DSM, as they provide a more comprehensive and accurate understanding of mental distress and its treatment ( 96 ). Moreover, the DSM’s conceptualization as a major determinative classification system could be standing at the core of psychiatry’s “identity crisis”, where checklists of symptoms replaced thoughtful diagnoses despite after decades of brain research, no biomarker has been established for any disorders defined in the DSM ( 10 , 97 ).

Design approaches can help DSM task forces prioritize integrating lived experiences to co-create a framework that can accommodate a range of perspectives to make it viable as a conversation piece. As DSM classifications do not reflect reality ( 98 ), listening to people with firsthand experiences is necessary. The CHIME framework – a conceptual framework of people’s experiences of recovery – shows, for example, a clear need to diagnose not solely based on symptoms but also considering people’s stages in their journey of personal recovery ( 80 ). Further, bottom-up research shows that the lived experience perspective of psychosis can seem very different compared to conventional psychiatric conceptualizations ( 82 ). This is also the case for the lived experience of depression ( 99 ). Design approaches can ensure that such much-needed perspectives and voices are adhered to in developing meaningful innovations ( 88 ), which brings us back to the design of the DSM. Although the DSM aims to conceptualize the reality of mental distress, engaging people with experiences of living with mental distress has never been prioritized by the DSM task force as an important epistemic resource. This is evidenced by the historically low engagement of people with lived experiences and their contexts. For example, although “individuals with mental disorders and families of individuals with mental disorders” participated in providing feedback in the DSM-5 revisions process ( 14 ), when and how they were involved, what feedback they gave, and how this was incorporated are not described. According to the Involvement Matrix ( 100 ) — a matrix that can be used to assess the contribution of patients in research and design —, giving feedback can be classified as ‘listeners’ or ‘co-thinkers,’ which are both low-involvement roles. Moreover, a review of the members of the DSM task forces and working groups listed in the introductions of the DSMs shows patients have never been part of the DSM task force and thus never been part of the decision-making process ( 96 ). Human-centered design is difficult to achieve when people with lived experience are not involved from preparation to implementation but are only asked to give feedback on expert consensus ( 88 ).

In the participation era, using a design approach in mental health care without engaging important stakeholders can be problematic. For example, it is evident that the involvement of people with lived experience changes the nature of an intervention dramatically, as people’s unique first-hand experiences, insights about mental states, and individual meaning and needs are often different in design activities as opposed to what general scientific and web-related resources suggest ( 101 , 102 ). Further, clear differences are reported around designers, researchers, and clinicians on one side and service user ideas of meaningful interventions on the other ( 102 , 103 ). Thus, the meaningful engagement of people with lived experience in design processes always exposes gaps between general research and the interests and lives of service users ( 104 ). This makes the participation of people with lived experience in developing innovative concepts — and, as such, in the conceptual design of DSM spectra of mental distress — essential because their absence in design processes may lead to ineffective outcomes ( 102 ). This design perspective may explain some of the negative effects of the DSM. The classifications aimed to be empirical constructs reflecting reality, yet phenomena such as reification and the classificatory looping effect emerged ( 42 , 51 ). From a design perspective, the emergence of these effects may have a simpler explanation than previously presumed: the premature over-commitment in the DSM’s design processes without input from individuals with firsthand experiences.

3.3 Shifting the premature over-commitment to iterative exploration

The centrist development approach used to design the DSM implicitly frames people with mental distress as ‘ordinary people,’ resulting in ‘average solutions’ because their experiences are decontextualized and lumped together on a group level — eventually leading to general descriptions for a universal appliance. Instead, a more human-centered iterative design process in which people with lived experience play an important role, preferably as decision-makers, can promote the design of spectra of mental distress that leave room for idiosyncrasies that correspond with people’s living environments on an individual level. This can potentially ensure that they are actually helpful for shared decision-making between patients and professionals and resonate in person-centered mental health care. A design approach is feasible for this aim because design processes are not searching for a singular ‘truth’ but rather exploring the multiple ‘truths’ that may be relevant in different contexts ( 105 ). This can be of added value to conceptualizing spectra of mental distress, which is known to have characteristics that overlap between people but also to have a unique phenomenology and contextual foundation for each individual — in the case of mental distress, there literally are multiple truths dependable on who and what you ask in what time and place. Furthermore, design approaches enable exploration and discovery ( 106 ). Designers consistently draw cues from the environment and introduce new variables into the same environment to eventually discover what does and does not work ( 107 ). This explorative attitude also ensures the discovery of unique insights, such as people’s experiential knowledge and contexts. Therefore, from a design perspective, predetermining solutions might be ineffective for arriving at DSM innovation. This is, for example, aptly described by Owens et al. ( 101 ):

“… the iterative nature of the participatory process meant that, although a preliminary programme for the whole workshop series was drawn up at the outset, plans had to be revised in response to the findings from each session. The whole process required flexibility, a constantly open mind and a willingness to embrace the unexpected”.

These insights illustrate the core of design that can guide the development of future DSM iterations: design enables the task force to learn about mental health problems without an omniscient perspective by iteratively developing and testing conceptualizations in the environment in partnership with the target group. As participatory design studies consistently demonstrate, solutions cannot be predetermined solely based on research and resources. The involvement of individuals with lived experience and their contexts invariably uncovers crucial serendipitous insights that challenge the perspectives on the problem. This can expose important misconceptions, such as the tendency to underestimate the complexity of human experience and decontextualize it from its environment.

3.4 Insights that could inform a procedure for co-designing spectra of mental distress

People with lived experience need to be highly involved in developing meaningful spectra of mental distress to guide conversations in clinical practice. As we now have a comprehensive understanding of what design approaches can offer to the development procedure of a lived experience-informed DSM, we will highlight these insights in this paragraph.

3.4.1 Balance academic research with lived experience insights

In the design procedure of a future DSM, academic research can be used to learn about people’s experiences of mental distress but never as the source alone for the development of spectra of mental distress. In this way, designers and researchers in mental health care need to involve people with lived experience at the heart of design processes as partners and come to unique insights together without an omniscient perspective. The aim should not be to design general descriptions but to design spectra that are flexible enough to adapt to local needs and constraints for the various parties using them yet robust enough to maintain a common identity across different locations. This allows the DSM to have different meanings in different social worlds, while at the same time, their structure is common enough for more than one world to recognize them.

3.4.2 Prevent premature overcommitment in the design process

Conceptualizations of spectra of mental distress must not be predetermined, and there should be no overcommitment to concepts in the early phases of the project. Thus, the task force should avoid viewing mental distress too narrowly, too early on in the process. This enables the evolution of lived experience-based spectra in an iterative design- and test process. The starting point should be an open representation of mental distress and discover together with people with lived experience how this could be best conceptualized and what language should be used. This allows room for exploring and discovering what works and aligns with patients’ needs and experiences in their living environments and professionals’ needs in their work environments.

3.4.3 Designing and testing is also a form of research

Researchers and designers should realize that designing and testing conceptualizations in partnership with people with lived experience also results in unique knowledge that can guide the development — designing and testing the developed concepts is a form of research. For example, exploring if a certain designed spectrum resonates as a conversation piece between patients and professionals in clinical practice provides qualitative insights that cannot be predicted beforehand. In this way, science and design can complement the innovation of the DSM: science benefits from a design approach, while design benefits from scientific methods ( 108 ). Flexible navigation between design and science would indicate that the developed DSM can be meaningful as a conversation piece in clinical practice.

3.4.4 Good design comes before effective science

Good design comes before effective science, as innovations are useless if not used, even if they are validated by science ( 85 ). Although the development of the DSM is often described as a scientific process, our analysis indicates that it is more accurately described as a design process. As a design process, it requires a methodologically sound design approach that is suitable for involving patients and people with lived experience. Co-design is a great contender for this purpose, as a systematic review showed this approach had the highest level of participant involvement in mental health care innovation ( 89 ). Although people with lived experience have never been involved as decision-makers, this should be the aim of the design process of a novel DSM in the participation era. This promotes lived experience leadership in design and, ultimately, contributes to more effective science.

3.4.5 Avoid tokenism and co-optation

Involving people with lived experience as decision-makers in redesigning the DSM must avoid tokenism and co-optation and address power imbalances. The first step that the task force can take is to use the Involvement Matrix ( 100 ) together with people with lived experiences to systematically and transparently plan, reflect, and report on everyone’s contribution to the design process. This has not been prioritized in the past DSM revisions. In the end, transparency and honesty about collaboration can support the empowerment of people with firsthand perspectives and shift the power imbalance towards co-creation for more human-centered mental health care. This is needed, as the involvement of people with lived experience in design and research processes is currently too low and obscured by vague terms and bad reporting.

4 Discussion and conclusion

In this hypothesis and theory paper, we have argued that the current role of the DSM, as an operating manual for professionals, can be reconsidered as a boundary object and conversation piece for patients and professionals in clinical practice that stimulates dialogue about mental distress. In this discussion, we will address five themes. First, while we argued that research acknowledges the absence of empirical support for biological causation, we believe characterizing the DSM as entirely non-empirical may be incorrect. Second, we discuss our perspective on balancing between a too-narrow medical perspective and a too-broadly individualized perspective. Third, we discuss why mental health care also needs novel methods for inquiry if the DSM is reconsidered as a conversation piece. Fourth, we discuss that while we are certain that design approaches can be fruitful for redesigning the DSM, some challenges regarding tokenism, co-optation must be addressed. We conclude by examining various methodological challenges and offering recommendations for the co-design process of the DSM.

4.1 Redesigning instead of discarding the DSM

The DSM is too deeply entrenched in mental health care to discard it simply. The DSM is embedded in not only mental health care but also society. For instance, a DSM classification is necessary in the Netherlands to get mental health care reimbursement, qualify for additional education test time, or receive subsidized assisted living. Moreover, it is ingrained in research and healthcare funding, making it unproductive and somewhat dangerous to discard without an alternative, as it may jeopardize access to care and impact insurance coverage for treatment and services that people with mental distress need. Therefore, we posited that instead of discarding the DSM, its role should be reconsidered in a mental health care system centered around shared decision-making and co-creation to eliminate pervasive effects such as the disengagement of patients, reification, disorderism, and the psychiatrization of society. However, the DSM categories are not entirely a priori constructed as is sometimes claimed, as the psychiatric symptom space and diagnostic categories took shape in the late nineteenth century through decades of observation ( 109 ).

While this adds important nuance to the idea that the design of the DSM is entirely non-empirical, it does not invalidate the argument that the DSM design is grounded in a potentially false ontology ( 64 ). Though the lack of evidence does not necessarily indicate evidence of absence, and the biological context in some way plays a role, research shows various other dimensions of life — including the social, historical, relational, environmental, and more — also influence mental distress, yet are significantly underemphasized in its current design. We believe that we showed this manifests itself most prominently in the various highly arbitrary classification designs that can confuse the professional and the patient and appear limited in providing meaningful guidance for clinical practice, design, and research. That is why we have proposed redesigning the next iteration of the DSM to primarily focus on formulating a set of spectra of distress. Reconsidering the DSM leverages one of its biggest strengths: the DSM is not bound by an analytic procedure but rather is guided by scientific debate ( 17 ). Further, developments and amendments to psychiatric classification systems have always reflected wider social and cultural developments ( 110 ). The recognition, implementation, and impact of the DSM in Western countries can even be seen as a reason not to focus on developing alternative models but rather to redesign the DSM so that it conceptually aligns with the social developments, scientific findings, and needs of people in the 21st century, as it is already deeply embedded in systems. Given that DSM classifications are now recognized as inaccurate depictions of the reality of mental distress ( 98 ) and that, at the same time, mental health care is shifting towards person-centeredness and shared decision-making, we believe the proposals in this article are not radical but rather the most meaningful way forward to accommodate diverse perspectives.

4.2 Balancing between a too-narrow medical categorization and a too-broadly individualized approach

From a classical psychopathological perspective, integrating the lived experiences of those with mental distress into the redevelopment of the DSM as a boundary object presents certain conceptual challenges. For example, uncritically overemphasizing individual experiences might lead to an underappreciation of psychopathological manifestations like, for example, altered perceptions. Conversely, excluding people with lived experience from the DSM’s design processes has resulted in its own conceptual and epistemic issues, such as undervaluing the idiographic, contextual, and phenomenological aspects of individual mental distress. Therefore, we argue that achieving a balance between these differing but crucial perspectives should result from a co-design procedure for a revised DSM. Determining this balance before obtaining results from such a process is too premature and arbitrary and would contradict our recommendation to prevent over-commitment in the early stages of the design process. As people with lived experience were never previously involved, it is impossible to predict the outcomes of a co-design procedure or hypothesize about a clear distinction between these perspectives in the DSM’s conceptual development beforehand. As seen in past iterations, prematurely drawing rigid lines could hinder the design process and result in design fixation. From the perspective of boundary objects, the DSM cannot have one dominant perspective if it is to function effectively. All stakeholders must be able to give meaning to the spectra of mental distress from their own activity systems, and these perspectives should be equal in order to create a shared awareness of the different perspectives involved. A DSM designed as a boundary object triggers dialogical learning mechanisms, ensuring the multiple perspectives are harmonized rather than adjusted to fit one another, ensuring no single perspective prevails over the others or consensus is pursued ( 71 , 111 ).

4.3 Novel methods for inquiry to accompany the reconsidered role of the DSM

If the DSM is reconsidered and designed as a conversation piece and classifications are replaced by spectra, in clinical practice, a unique language needs to be co-developed between the patient and the professional, and an equal relationship is important to ally. For example, if we consider the person-specific meanings of altered perceptions, they need to be explored, as they have clinical relevance. However, for such purposes, current diagnostic methods in clinical practice are limiting because they are highly linguistic and tailored to classification systems and the needs and praxis of the professionals. This can impede the DSM’s effectiveness as a tool for dialogue. Expressing the uniqueness of an experience of mental distress is difficult — especially during a mental crisis — let alone effectively communicating it to a professional. While people with mental distress can effectively communicate their behaviors and complaints, which fits the current use of the DSM, people have far more embodied and experiential knowledge of their distress. How people cope with their mental distress in the contexts they are living in is very difficult to put into words without first making these personal and contextual insights tangible ( 41 ), yet this is essential information for when the DSM is used as a boundary object and conversation piece. To accommodate the patient in making this knowledge tangible, the professional becomes more of a facilitator than an expert, emphasizing therapeutic relationships and the healing effects of ritualized care interactions ( 39 ). This transformation requires novel co-creative methods for inquiry ( 41 ) and professional training ( 39 ). Therefore, expanding the diagnostic toolkit with innovative and creative tools and embracing professionals such as art therapists, social workers, and advanced nurse practitioners to enable and support patients to convey their narratives and needs in their own way is essential if the DSM is to be used as a boundary object and conversation piece.

4.4 Promoting lived experience leadership in the co-design procedure

Despite longstanding calls for the APA to include people with lived experience in the decision-making processes for diagnostic criteria, the DSM-5 task force did not accept this inclusion. The task force believed incorporating these perspectives could compromise objectivity in the scientific process ( 96 ). This mindset ensures that research, design, and practice remain predominantly shaped by academics and professionals, causing conventional mental health care to perpetuate itself. It continues to repeat the same approaches and consequently achieves the same results. Therefore, people with lived experience should have more influence in the participation era to accelerate change in mental health care. This proposition comes with some challenges regarding power imbalances that need addressing. While it is acknowledged that the involvement of individuals with lived experience yields unique insights and can serve as strong collaborators and knowledgeable contributors, they are never given decision-making authority in design processes in mental health care ( 88 , 89 , 92 ) or in the DSM’s development processes ( 96 ). This lack of authority impedes lived experience leadership ( 91 , 112 ) and subsequently stands in the way of effectively reconsidering and redesigning the DSM. To avoid tokenism, the DSM revision process should not settle for low engagement and involvement but set the bar higher by redressing power imbalances ( 113 ). Furthermore, in the co-design process of the DSM, the task force should not view objectivity as the opposite of subjectivity or strive for consensus. Instead, they should value group discussions and disagreements, encouraging stakeholders to debate and explore the sources of their differing perspectives and knowledge ( 96 ). Shifting towards lived experience leadership starts with perceiving and engaging people with lived experiences of mental distress as experts of their experiences in iterative design and research processes and giving them this role in revising the DSM.

4.5 Methodological considerations for a co-design procedure of the DSM

Merely positioning people with lived experience as partners and decision-makers is insufficient; there are also significant methodological concerns regarding the execution of design research in mental health care. Although iteration and participation are essential for design in mental health care, as designers focus on the unmet needs of service users and ways to improve care ( 114 ), research shows design is not always executed iteratively, and end users are not always involved. For example, about one-third of projects that designed mental health interventions did not adopt an iterative process ( 85 ). The engagement of end users in design processes in mental health is also not yet a common practice. For instance, a systematic review of serious games in mental health for anxiety and depression found that only half of these games, even while reporting using a participatory approach, were designed with input from the intended end-users ( 115 ). A systematic review of design processes that aimed to design innovations for people with psychotic symptoms overlaps these findings, as less than half of the studies demonstrated a high level of participant involvement in their design processes ( 89 ).

The low level of involvement and lack of iterative approaches in mental health care design offer valuable insights for future processes. If the DSM task force aims to adopt a co-design approach, it should incorporate these lessons to enhance design effectiveness. First, the task force must understand that design has a different aim, culture, and methods than the sciences ( 116 ). The scientific approach typically implies investigating the natural world through controlled experiments, classifications, and analysis, emphasizing objectivity, rationality, neutrality, and a commitment to truth. In contrast, a design approach focuses on studying the artificial world, employing methods such as modeling, pattern formation, and synthesis, guided by core values of practicality, ingenuity, empathy, and concern for appropriateness. Second, the task force should consider the known challenges they will encounter and need to navigate to let the paradigms be complementary in practice ( 117 ). Further, the task force should consider that the nature of design is exploratory, iterative, uncertain, and a social form of inquiry and synthesis that is never perfect and never quite finished ( 84 ). This requires tolerating ambiguity and having trust ( 101 ). Lastly, more transparency in the participatory work of the task force is called for, beginning with being honest, being detailed, addressing power imbalances, being participatory in reporting the participatory approach, and being excited and enthusiastic about going beyond tokenistic engagement ( 118 ).

Despite these challenges, transforming psychiatric diagnoses by reconsidering and redesigning the DSM as a boundary object and conversation piece could be a step in the right direction. This would shift the power balance towards shared ownership in a participation era that fosters dialogue instead of diagnosis. We hope this hypothesis and theory paper can give decisive impulses to the much-needed debate on and development of psychiatric diagnoses and, in the end, contribute to lived experience-informed psychiatric epistemology. Furthermore, as a product of an equal co-production process between various disciplines and types of knowledge, this paper shows it is possible to harmonize perspectives on a controversial topic such as the DSM.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author/s.

Author contributions

LV: Conceptualization, Methodology, Project administration, Writing – original draft, Writing – review & editing. GT: Conceptualization, Methodology, Visualization, Writing – original draft, Writing – review & editing. JVO: Conceptualization, Writing – original draft, Writing – review & editing. SM: Conceptualization, Writing – original draft, Writing – review & editing. JV: Writing – original draft, Writing – review & editing. NB: Writing – original draft, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. We appreciate the financial support of the FAITH Research Consortium, GGZ-VS University of Applied Science, as well as from the NHL Stenden University of Applied Sciences PhD program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Acknowledgments

We thank the reviewers for their thorough reading of our manuscript and valuable comments, which improved the quality of our hypothesis and theory paper.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

1. Fischer BA. A review of american psychiatry through its diagnoses. J Nervous Ment Dis . (2012) 200:1022–30. doi: 10.1097/NMD.0b013e318275cf19

CrossRef Full Text | Google Scholar

2. Grob GN. Origins of DSM-I: a study in appearance and reality. Am J Psychiatry . (1991) 148:421–31. doi: 10.1176/ajp.148.4.421

PubMed Abstract | CrossRef Full Text | Google Scholar

3. Brendel DH. Healing psychiatry: A pragmatic approach to bridging the science/humanism divide. Harvard Rev Psychiatry . (2004) 12:150–7. doi: 10.1080/10673220490472409

4. Braslow JT. Therapeutics and the history of psychiatry. Bull History Med . (2000) 74:794–802. doi: 10.1353/bhm.2000.0161

5. Kawa S, Giordano J. A brief historicity of the Diagnostic and Statistical Manual of Mental Disorders: Issues and implications for the future of psychiatric canon and practice. Philosophy Ethics Humanities Med . (2012) 7:2. doi: 10.1186/1747-5341-7-2

6. Mayes R, Horwitz AV. DSM-III and the revolution in the classification of mental illness. J History Behav Sci . (2005) 41:249–67. doi: 10.1002/(ISSN)1520-6696

7. American Psychiatric Association. Diagnostic & Statistical manual of Mental Disorders . 3rd edition. Washington, D.C: American Psychiatric Association (1980).

Google Scholar

8. Gambardella A. Science and innovation: the US pharmaceutical industry during the 1980s . New York: Cambridge University Press (1995). doi: 10.1017/CBO9780511522031

9. Klerman GL, Vaillant GE, Spitzer RL, Michels R. A debate on DSM-III. Am J Psychiatry . (1984) 141:539–53. doi: 10.1176/ajp.141.4.539

10. Scull A. American psychiatry in the new millennium: a critical appraisal. psychol Med . (2021) 51:1–9. doi: 10.1017/S0033291721001975

11. American Psychiatric Association. Diagnostic and statistical manual of mental disorders . 4th edition. Washington, D.C: American Psychiatric Association (1994).

12. Shaffer D. A participant’s observations: preparing DSM-IV. Can J Psychiatry . (1996) 41:325–9. doi: 10.1177/070674379604100602

13. Follette WC, Houts AC. Models of scientific progress and the role of theory in taxonomy development: A case study of the DSM. J Consulting Clin Psychol . (1996) 64:1120–32. doi: 10.1037//0022-006X.64.6.1120

14. American Psychiatric Association. Diagnostic and statistical manual of mental disorders: DSM-5-TR . 5th ed. Washington, Dc: American Psychiatric Association Publishing (2022). doi: 10.1176/appi.books.9780890425787

15. Pickersgill MD. Debating DSM-5: diagnosis and the sociology of critique. J Med Ethics . (2013) 40:521–5. doi: 10.1136/medethics-2013-101762

16. Hyman SE. The diagnosis of mental disorders: the problem of reification. Annu Rev Clin Psychol . (2010) 6:155–79. doi: 10.1146/annurev.clinpsy.3.022806.091532

17. Zachar P, Kendler KS. Psychiatric disorders: A conceptual taxonomy. Am J Psychiatry . (2007) 164:557–65. doi: 10.1176/ajp.2007.164.4.557

18. Francis A, First MB, Pincus HA. DSM-IV Guidebook . Washington DC: American Psychiatric Association (1995).

19. Spitzer RL, Williams JBW, Endicott J. Standards for DSM-5 reliability. Am J Psychiatry . (2012) 169:537–7. doi: 10.1176/appi.ajp.2012.12010083

20. Gómez-Carrillo A, Kirmayer LJ, Aggarwal NK, Bhui KS, Fung KP-L, Kohrt BA, et al. Integrating neuroscience in psychiatry: a cultural–ecosocial systemic approach. Lancet Psychiatry . (2023) 10:296–304. doi: 10.1016/s2215-0366(23)00006-8

21. Morgan C, Charalambides M, Hutchinson G, Murray RM. Migration, ethnicity, and psychosis: toward a sociodevelopmental model. Schizophr Bull . (2010) 36:655–64. doi: 10.1093/schbul/sbq051

22. Howes OD, Murray RM. Schizophrenia: an integrated sociodevelopmental-cognitive model. Lancet . (2014) 383:1677–87. doi: 10.1016/S0140-6736(13)62036-X

23. Alegría M, NeMoyer A, Falgàs Bagué I, Wang Y, Alvarez K. Social determinants of mental health: where we are and where we need to go. Curr Psychiatry Rep . (2018) 20. doi: 10.1007/s11920-018-0969-9

24. Jeste DV, Pender VB. Social determinants of mental health. JAMA Psychiatry . (2022) 79:283–4. doi: 10.1001/jamapsychiatry.2021.4385

25. Huggard L, Murphy R, O’Connor C, Nearchou F. The social determinants of mental illness: A rapid review of systematic reviews. Issues Ment Health Nurs . (2023) 44:1–11. doi: 10.1080/01612840.2023.2186124

26. Kirkbride JB, Anglin DM, Colman I, Dykxhoorn J, Jones PB, Patalay P, et al. The social determinants of mental health and disorder: evidence, prevention and recommendations. World psychiatry: Off J World Psychiatr Assoc (WPA) . (2024) 23:58–90. doi: 10.1002/wps.21160

27. Alon N, Macrynikola N, Jester DJ, Keshavan M, Reynolds ICF, Saxena S, et al. Social determinants of mental health in major depressive disorder: umbrella review of 26 meta-analyses and systematic reviews. Psychiatry Res . (2024) 335:115854. doi: 10.1016/j.psychres.2024.115854

28. Read J, Bentall RP. Negative childhood experiences and mental health: theoretical, clinical and primary prevention implications. Br J Psychiatry . (2012) 200:89–91. doi: 10.1192/bjp.bp.111.096727

29. van Os J, Kenis G, Rutten BPF. The environment and schizophrenia. Nature . (2010) 468:203–12. doi: 10.1038/nature09563

30. Köhne A, de Graauw LP, Leenhouts-van der Maas R, van Os J. Clinician and patient perspectives on the ontology of mental disorder: a qualitative study. Front Psychiatry . (2023) 14:1081925. doi: 10.3389/fpsyt.2023.1081925

31. Richter D, Dixon J. Models of mental health problems: a quasi-systematic review of theoretical approaches. J Ment Health . (2022) 32:1–11. doi: 10.1080/09638237.2021.2022638

32. World Health Organization. Guidance on community mental health services: Promoting person-centred and rights-based approaches (2021). Available online at: https://www.who.int/publications/i/item/9789240025707 .

33. Huber M, Knottnerus JA, Green L, Horst HVD, Jadad AR, Kromhout D, et al. How should we define health? BMJ . (2011) 343:d4163–3. doi: 10.1136/bmj.d4163

34. Davidson L. The recovery movement: implications for mental health care and enabling people to participate fully in life. Health Affairs . (2016) 35:1091–7. doi: 10.1377/hlthaff.2016.0153

35. Dixon LB, Holoshitz Y, Nossel I. Treatment engagement of individuals experiencing mental illness: review and update. World Psychiatry . (2016) 15:13–20. doi: 10.1002/wps.20306

36. Karbouniaris S. Let’s tango! Integrating professionals’ lived experience in the tranformation of mental health services. (2023). Available online at: https://hdl.handle.net/1887/3640655 .

PubMed Abstract | Google Scholar

37. Elwyn G, Laitner S, Coulter A, Walker E, Watson P, Thomson R. Implementing shared decision making in the NHS. BMJ . (2010) 341:c5146–6. doi: 10.1136/bmj.c5146

38. Oueslati R, Woudstra AJ, Alkirawan R, Reis R, Zaalen Yv, Slager MT, et al. What value structure underlies shared decision making? A qualitative synthesis of models of shared decision making. Patient Educ Couns . (2024) 124:108284. doi: 10.1016/j.pec.2024.108284

39. van Os J, Guloksuz S, Vijn TW, Hafkenscheid A, Delespaul P. The evidence-based group-level symptom-reduction model as the organizing principle for mental health care: time for change? World Psychiatry . (2019) 18:88–96. doi: 10.1002/wps.20609

40. Meerman S. ADHD and the power of generalization: exploring the faces of reification. researchrugnl . (2019). doi: 10.33612/diss.84379221

41. Veldmeijer L, Terlouw G, van ‘t VJ, van Os J, Boonstra N. Design for mental health: can design promote human-centred diagnostics? Design Health . (2023) 7:1–19. doi: 10.1080/24735132.2023.2171223

42. te Meerman S, Freedman JE, Batstra L. ADHD and reification: Four ways a psychiatric construct is portrayed as a disease. Front Psychiatry . (2022) 13:1055328. doi: 10.3389/fpsyt.2022.1055328

43. Guloksuz S, van Os J. The slow death of the concept of schizophrenia and the painful birth of the psychosis spectrum. psychol Med . (2017) 48:229–44. doi: 10.1017/S0033291717001775

44. Os Jv, Scheepers F, Milo M, Ockeloen G, Guloksuz S, Delespaul P. It has to be better, otherwise we will get stuck.” A Review of Novel Directions for Mental Health Reform and Introducing Pilot Work in the Netherlands. Clin Pract Epidemiol Ment Health . (2023) 19. doi: 10.2174/0117450179271206231114064736

45. Bowker GC. Susan Leigh Star. Sorting Things Out: Classification and Its Consequences (Inside technology) . Cambridge, MA: Mit Press (1999). doi: 10.7551/mitpress/6352.001.0001

46. Perkins A, Ridler J, Browes D, Peryer G, Notley C, Hackmann C. Experiencing mental health diagnosis: a systematic review of service user, clinician, and carer perspectives across clinical settings. Lancet Psychiatry . (2018) 5:747–64. doi: 10.1016/S2215-0366(18)30095-6

47. Ben-Zeev D, Young MA, Corrigan PW. DSM-V and the stigma of mental illness. J Ment Health . (2010) 19:318–27. doi: 10.3109/09638237.2010.492484

48. Thornicroft G. Shunned: discrimination against people with mental illness . Oxford: Oxford University Press (2009).

49. Köhne ACJ. The ontological status of a psychiatric diagnosis: the case of neurasthenia. Philosophy Psychiatry Psychol . (2019) 26:E–1-E-11. doi: 10.1353/ppp.2019.0008

50. Hacking I. Kinds of people: moving targets. Proceedings of the British Academy . Oxford: Oxford University Press Inc (2007). doi: 10.5871/bacad/9780197264249.003.0010

51. Hacking I. The social construction of what? Harvard: Harvard University Press (1999).

52. Wienen AW, Sluiter MN, Thoutenhoofd E, de Jonge P, Batstra L. The advantages of an ADHD classification from the perspective of teachers. Eur J Special Needs Educ . (2019) 34:1–14. doi: 10.1080/08856257.2019.1580838

53. Franz DJ, Richter T, Lenhard W, Marx P, Stein R, Ratz C. The influence of diagnostic labels on the evaluation of students: a multilevel meta-analysis. Educ Psychol Rev . (2023) 35. doi: 10.1007/s10648-023-09716-6

54. Köhne ACJ. In search of a better ontology of mental disorder. (2022). doi: 10.33540/1591

55. de Ridder B, van Hulst BM. Disorderism: what it is and why it’s a problem. Tijdschr Psychiatr . (2023) 65:163–6.

56. Kazda L, Bell K, Thomas R, McGeechan K, Sims R, Barratt A. Overdiagnosis of Attention-Deficit/Hyperactivity Disorder in children and adolescents. JAMA Network Open . (2021) 4. doi: 10.1001/jamanetworkopen.2021.5335

57. Beeker T, Mills C, Bhugra D, te Meerman S, Thoma S, Heinze M, et al. Psychiatrization of society: A conceptual framework and call for transdisciplinary research. Front Psychiatry . (2021) 12:645556. doi: 10.3389/fpsyt.2021.645556

58. Os Jv, Guloksuz S. Population salutogenesis—The future of psychiatry? JAMA Psychiatry . (2024) 81:115–5. doi: 10.1001/jamapsychiatry.2023.4582

59. Star SL, Griesemer JR. Institutional ecology, `Translations’ and boundary objects: amateurs and professionals in berkeley’s museum of vertebrate zoology, 1907-39. Soc Stud Sci . (1989) 19:387–420. doi: 10.1177/030631289019003001

60. Marsman A, Pries L-K, ten Have M, de Graaf R, van Dorsselaer S, Bak M, et al. Do current measures of polygenic risk for mental disorders contribute to population variance in mental health? Schizophr Bull . (2020). doi: 10.1093/schbul/sbaa086

61. van Os J. Personalized psychiatry: Geen vervanger van persoonlijke psychiatrie. Tijdschrift voor Psychiatr . (2018) 60:199–204.

62. Hyman SE. Psychiatric disorders: grounded in human biology but not natural kinds. Perspect Biol Med . (2021) 64:6–28. doi: 10.1353/pbm.2021.0002

63. Schleim S. Why mental disorders are brain disorders. And why they are not: ADHD and the challenges of heterogeneity and reification. Front Psychiatry . (2022) 13:943049. doi: 10.3389/fpsyt.2022.943049

64. Köhne ACJ. The relationalist turn in understanding mental disorders: from essentialism to embracing dynamic and complex relations. Philosophy Psychiatry Psychol . (2020) 27:119–40. doi: 10.1353/ppp.2020.0020

65. Terlouw G, Veer JTv’t, Prins JT, Kuipers DA, Pierie J-PEN. Design of a digital comic creator (It’s me) to facilitate social skills training for children with autism spectrum disorder: design research approach. JMIR Ment Health . (2020) 7:e17260. doi: 10.2196/17260

66. Terlouw G, Kuipers D, van ’t Veer J, Prins JT, Pierie JPEN. The development of an escape room–based serious game to trigger social interaction and communication between high-functioning children with autism and their peers: iterative design approach. JMIR Serious Games . (2021) 9:e19765. doi: 10.2196/19765

67. Kuipers DA, Terlouw G, Wartena BO, Prins JT, Pierie JPEN. Maximizing authentic learning and real-world problem-solving in health curricula through psychological fidelity in a game-like intervention: development, feasibility, and pilot studies. Med Sci Educator . (2018) 29:205–14. doi: 10.1007/s40670-018-00670-5

68. Kajamaa A. Boundary breaking in a hospital. Learn Organ . (2011) 18:361–77. doi: 10.1108/09696471111151710

69. Sajtos L, Kleinaltenkamp M, Harrison J. Boundary objects for institutional work across service ecosystems. J Service Manage . (2018) 29:615–40. doi: 10.1108/JOSM-01-2017-0011

70. Jensen S, Kushniruk A. Boundary objects in clinical simulation and design of eHealth. Health Inf J . (2014) 22:248–64. doi: 10.1177/1460458214551846

71. Terlouw G, Kuipers D, Veldmeijer L, van ’t Veer J, Prins J, Pierie J-P. Boundary objects as dialogical learning accelerators for social change in design for health: systematic review. JMIR Hum Factors . (2021). doi: 10.2196/31167

72. Wiener HJD. Conversation pieces: the role of products in facilitating conversation. Dukespace . (2017). Available online at: https://hdl.handle.net/10161/14430 .

73. Jaspers K. General psychopathology . Baltimore, Md: Johns Hopkins University Press (1998).

74. Parnas J, Sass LA, Zahavi D. Rediscovering psychopathology: the epistemology and phenomenology of the psychiatric object. Schizophr Bull . (2012) 39:270–7. doi: 10.1093/schbul/sbs153

75. Feyaerts J, Henriksen MG, Vanheule S, Myin-Germeys I, Sass LA. Delusions beyond beliefs: a critical overview of diagnostic, aetiological, and therapeutic schizophrenia research from a clinical-phenomenological perspective. Lancet Psychiatry . (2021) 8:237–49. doi: 10.1016/S2215-0366(20)30460-0

76. Ritunnano R, Kleinman J, Whyte Oshodi D, Michail M, Nelson B, Humpston CS, et al. Subjective experience and meaning of delusions in psychosis: a systematic review and qualitative evidence synthesis. Lancet Psychiatry . (2022) 9:458–76. doi: 10.1016/S2215-0366(22)00104-3

77. Köhne ACJ, van Os J. Precision psychiatry: promise for the future or rehash of a fossilised foundation? psychol Med . (2021) 51:1409–11. doi: 10.1017/S0033291721000271

78. Boevink WA. From being a disorder to dealing with life: an experiential exploration of the association between trauma and psychosis. Schizophr Bull . (2005) 32:17–9. doi: 10.1093/schbul/sbi068

79. Slade M, Longden E. Empirical evidence about recovery and mental health. BMC Psychiatry . (2015) 15. doi: 10.1186/s12888-015-0678-4

80. Leamy M, Bird V, Boutillier CL, Williams J, Slade M. Conceptual framework for personal recovery in mental health: systematic review and narrative synthesis. Br J Psychiatry . (2011) 199:445–52. doi: 10.1192/bjp.bp.110.083733

81. Groot PC, van Os J. How user knowledge of psychotropic drug withdrawal resulted in the development of person-specific tapering medication. Ther Adv Psychopharmacol . (2020) 10:204512532093245. doi: 10.1177/2045125320932452

82. Fusar-Poli P, Estradé A, Stanghellini G, Venables J, Onwumere J, Messas G, et al. The lived experience of psychosis: A bottom-up review co-written by experts by experience and academics. World Psychiatry . (2022) 21:168–88. doi: 10.1002/wps.20959

83. Jakobsson C, Genovesi E, Afolayan A, Bella-Awusah T, Omobowale O, Buyanga M, et al. Co-producing research on psychosis: a scoping review on barriers, facilitators and outcomes. Int J Ment Health Syst . (2023) 17. doi: 10.1186/s13033-023-00594-7

84. Orlowski S, Matthews B, Bidargaddi N, Jones G, Lawn S, Venning A, et al. Mental health technologies: designing with consumers. JMIR Hum Factors . (2016) 3. doi: 10.2196/humanfactors.4336

85. Vial S, Boudhraâ S, Dumont M. Human-centered design approaches in digital mental health interventions: exploratory mapping review. JMIR Ment Health . (2021) 9. doi: 10.2196/35591

86. Tindall RM, Ferris M, Townsend M, Boschert G, Moylan S. A first-hand experience of co-design in mental health service design: Opportunities, challenges, and lessons. Int J Ment Health Nurs . (2021) 30. doi: 10.1111/inm.12925

87. Schouten SE, Kip H, Dekkers T, Deenik J, Beerlage-de Jong N, Ludden GDS, et al. Best-practices for co-design processes involving people with severe mental illness for eMental health interventions: a qualitative multi-method approach. Design Health . (2022) 6:316–44. doi: 10.1080/24735132.2022.2145814

88. Veldmeijer L, Terlouw G, van Os J, van Dijk O, van ’t Veer J, Boonstra N. The involvement of service users and people with lived experience in mental health care innovation through design: systematic review. JMIR Ment Health . (2023) 10:e46590. doi: 10.2196/46590

89. Veldmeijer L, Terlouw G, van Os J, van ’t Veer J, Boonstra N. The frequency of design studies targeting people with psychotic symptoms and features in mental health care innovation: A research letter of a secondary data analysis. JMIR Ment Health . (2023) 11. doi: 10.2196/54202

90. Hawke LD, Sheikhan NY, Bastidas-Bilbao H, Rodak T. Experience-based co-design of mental health services and interventions: A scoping review. SSM Ment Health . (2024) 5:100309. doi: 10.1016/j.ssmmh.2024.100309

91. Scholz B, Gordon S, Happell B. Consumers in mental health service leadership: A systematic review. Int J Ment Health Nurs . (2016) 26:20–31. doi: 10.1111/inm.12266

92. Brotherdale R, Berry K, Branitsky A, Bucci S. Co-producing digital mental health interventions: A systematic review. Digital Health . (2024) 10. doi: 10.1177/20552076241239172

93. Scholz B. Mindfully reporting lived experience work. Lancet Psychiatry . (2024) 11:168–8. doi: 10.1016/S2215-0366(24)00007-5

94. Davis S, Pinfold V, Catchpole J, Lovelock C, Senthi B, Kenny A. Reporting lived experience work. Lancet Psychiatry . (2024) 11:8–9. doi: 10.1016/S2215-0366(23)00402-9

95. Palmer VJ, Bibb J, Lewis M, Densley K, Kritharidis R, Dettmann E, et al. A co-design living labs philosophy of practice for end-to-end research design to translation with people with lived-experience of mental ill-health and carer/family and kinship groups. Front Public Health . (2023) 11:1206620. doi: 10.3389/fpubh.2023.1206620

96. Tekin Ş. Participatory interactive objectivity in psychiatry. Philosophy Sci . (2022), 1–20. doi: 10.1017/psa.2022.47

97. Gardner C, Kleinman A. Medicine and the mind — The consequences of psychiatry’s identity crisis. New Engl J Med . (2019) 381:1697–9. doi: 10.1056/NEJMp1910603

98. Kendler KS. Potential lessons for DSM from contemporary philosophy of science. JAMA Psychiatry . (2022) 79:99. doi: 10.1001/jamapsychiatry.2021.3559

99. Fusar-Poli P, Estradé Andrés, Stanghellini G, Esposito CM, Rosfort René, Mancini M, et al. The lived experience of depression: a bottom-up review co-written by experts by experience and academics. World Psychiatry . (2023) 22:352–65. doi: 10.1002/wps.21111

100. Smits D-W, van Meeteren K, Klem M, Alsem M, Ketelaar M. Designing a tool to support patient and public involvement in research projects: the Involvement Matrix. Res Involvement Engagement . (2020) 6. doi: 10.1186/s40900-020-00188-4

101. Owens C, Farrand P, Darvill R, Emmens T, Hewis E, Aitken P. Involving service users in intervention design: a participatory approach to developing a text-messaging intervention to reduce repetition of self-harm. Health Expectations . (2010) 14:285–95. doi: 10.1111/hex.2011.14.issue-3

102. Nakarada-Kordic I, Hayes N, Reay SD, Corbet C, Chan A. Co-designing for mental health: creative methods to engage young people experiencing psychosis. Design Health . (2017) 1:229–44. doi: 10.1080/24735132.2017.1386954

103. Orlowski SK, Lawn S, Venning A, Winsall M, Jones GM, Wyld K, et al. Participatory research as one piece of the puzzle: A systematic review of consumer involvement in design of technology-based youth mental health and well-being interventions. JMIR Hum Factors . (2015) 2:e12. doi: 10.2196/humanfactors.4361

104. Gammon D, Strand M, Eng LS. Service users’ perspectives in the design of an online tool for assisted self-help in mental health: a case study of implications. Int J Ment Health Syst . (2014) 8. doi: 10.1186/1752-4458-8-2

105. Wendt T. Design for Dasein: understanding the design of experiences . San Bernardino, California: Thomas Wendt (2015).

106. Bateson G. Steps to an ecology of mind . Chicago: University Of Chicago Press (1972).

107. Schön DA. The Reflective Practitioner: How Professionals Think in Action . Aldershot, U.K: Ashgate, Cop (1991).

108. Verkerke GJ, van der Houwen EB, Broekhuis AA, Bursa J, Catapano G, McCullagh P, et al. Science versus design; comparable, contrastive or conducive? J Mechanical Behav Biomed Materials . (2013) 21:195–201. doi: 10.1016/j.jmbbm.2013.01.009

109. Zachar P. Psychopathology beyond psychiatric symptomatology. Philosophy Psychiatry Psychol . (2020) 27:141–3. doi: 10.1353/ppp.2020.0021

110. Foucault M. Madness and Civilization: A History of Insanity in the Age of Reason . London: Routledge (1961). doi: 10.4324/9780203278796

111. Star SL. This is not a boundary object: reflections on the origin of a concept. Science Technology Hum Values . (2010) 35:601–17. doi: 10.1177/0162243910377624

112. Jones N. Lived experience leadership in peer support research as the new normal. Psychiatr Serv . (2022) 73:125. doi: 10.1176/appi.ps.73201

113. Scholz B. We have to set the bar higher: towards consumer leadership, beyond engagement or involvement. Aust Health Rev . (2022) 46. doi: 10.1071/AH22022

114. Rivard L, Lehoux P, Hagemeister N. Articulating care and responsibility in design: A study on the reasoning processes guiding health innovators’ “care-making” practices. Design Stud . (2021) 72:100986. doi: 10.1016/j.destud.2020.100986

115. Dekker MR, Williams AD. The use of user-centered participatory design in serious games for anxiety and depression. Games Health J . (2017) 6:327–33. doi: 10.1089/g4h.2017.0058

116. Cross N. Designerly ways of knowing . London: Springer (2006).

117. Groeneveld B, Dekkers T, Boon B, D’Olivo P. Challenges for design researchers in healthcare. Design Health . (2018) 2:305–26. doi: 10.1080/24735132.2018.1541699

118. Scholz B, Stewart S, Pamoso A, Gordon S, Happell B, Utomo B. The importance of going beyond consumer or patient involvement to lived experience leadership. Int J Ment Health Nurs . (2023) 33:1–4. doi: 10.1111/inm.13282

Keywords: psychiatry, diagnosis, design, innovation, mental health care

Citation: Veldmeijer L, Terlouw G, van Os J, te Meerman S, van ‘t Veer J and Boonstra N (2024) From diagnosis to dialogue – reconsidering the DSM as a conversation piece in mental health care: a hypothesis and theory. Front. Psychiatry 15:1426475. doi: 10.3389/fpsyt.2024.1426475

Received: 01 May 2024; Accepted: 22 July 2024; Published: 06 August 2024.

Reviewed by:

Copyright © 2024 Veldmeijer, Terlouw, van Os, te Meerman, van ‘t Veer and Boonstra. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Lars Veldmeijer, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

To revisit this article, visit My Profile, then View saved stories .

• The Big Story
• Newsletters
• Steven Levy's Plaintext Column
• WIRED Classics from the Archive
• WIRED Insider
• WIRED Consulting

‘Gem’ of a Proof Breaks 80-Year-Old Record, Offers New Insights Into Prime Numbers

The original version of this story appeared in Quanta Magazine .

Sometimes mathematicians try to tackle a problem head on, and sometimes they come at it sideways. That’s especially true when the mathematical stakes are high, as with the Riemann hypothesis, whose solution comes with a $1 million reward from the Clay Mathematics Institute. Its proof would give mathematicians much deeper certainty about how prime numbers are distributed, while also implying a host of other consequences—making it arguably the most important open question in math.

Mathematicians have no idea how to prove the Riemann hypothesis. But they can still get useful results just by showing that the number of possible exceptions to it is limited. “In many cases, that can be as good as the Riemann hypothesis itself,” said James Maynard of the University of Oxford. “We can get similar results about prime numbers from this.”

In a breakthrough result posted online in May, Maynard and Larry Guth of the Massachusetts Institute of Technology established a new cap on the number of exceptions of a particular type, finally beating a record that had been set more than 80 years earlier. “It’s a sensational result,” said Henryk Iwaniec of Rutgers University. “It’s very, very, very hard. But it’s a gem.”

The new proof automatically leads to better approximations of how many primes exist in short intervals on the number line, and stands to offer many other insights into how primes behave.

A Careful Sidestep

The Riemann hypothesis is a statement about a central formula in number theory called the Riemann zeta function. The zeta (ζ) function is a generalization of a straightforward sum:

1 + 1/2 + 1/3 + 1/4 + 1/5 + ⋯.

This series will become arbitrarily large as more and more terms are added to it—mathematicians say that it diverges. But if instead you were to sum up

1 + 1/2 2 + 1/3 2 + 1/4 2 + 1/5 2 + ⋯ = 1 + 1/4 + 1/9+ 1/16 + 1/25 +⋯

you would get π 2 /6, or about 1.64. Riemann’s surprisingly powerful idea was to turn a series like this into a function, like so:

ζ( s ) = 1 + 1/2 s + 1/3 s + 1/4 s + 1/5 s + ⋯.

So ζ(1) is infinite, but ζ(2) = π 2 /6.

Things get really interesting when you let s be a complex number, which has two parts: a “real” part, which is an everyday number, and an “imaginary” part, which is an everyday number multiplied by the square root of −1 (or i , as mathematicians write it). Complex numbers can be plotted on a plane, with the real part on the x -axis and the imaginary part on the y -axis. Here, for example, is 3 + 4 i .

The zeta function takes points on the complex plane as inputs, and it produces other complex numbers as outputs. It turns out that for some complex numbers, the zeta function is equal to zero. Figuring out where those zeros are located on the complex plane is one of the most interesting questions in mathematics.

In 1859, Bernhard Riemann conjectured that all the zeros are concentrated on two lines. If you extend the zeta function so you can compute it for negative inputs, you’ll find that it equals zero for all negative even numbers: −2, −4, −6 and so on. This is relatively easy to show, so these are called trivial zeros. Riemann conjectured that all the other zeros of the function, called nontrivial zeros, have a real part of 1/2, and so are located on this vertical line.

This is the Riemann hypothesis, and proving it has been prohibitively difficult. Mathematicians know that every nontrivial zero must have a real part between zero and 1, but they can’t rule out that some zeros might have a real part of, say, 0.499.

What they can do is show that such zeros must be incredibly rare. In 1940, an English mathematician named Albert Ingham established an upper bound on the number of zeros whose real part is not equal to 1/2 that mathematicians continue to use as a point of reference today.

A few decades later, in the 1960s and ’70s, other mathematicians figured out how to translate Ingham’s result into statements about how clumped or spread out prime numbers are as you move further along the number line, and about other patterns they might form. Around the same time, mathematicians also introduced new techniques that improved Ingham’s bounds for zeros with a real part greater than 3/4.

But it turned out that the most important zeros to cap were those with a real part of exactly 3/4. “Lots of headline results about prime numbers were limited by our understanding of zeros with real part 3/4,” Maynard said.

About a decade ago, Maynard started thinking about how to improve Ingham’s estimate for those particular zeros. “It’s been one of my favorite problems in analytic number theory,” he said. “It always felt tempting that you just have to work a bit harder, and you’ll be able to get an improvement.” But year after year, no matter how many times he came back to it, he kept getting stuck. “It almost sucked you in, and it looked much more innocent than I think it was.”

Then, in early 2020, during a plane trip to a conference in Colorado, an idea came to him. Perhaps, Maynard thought, tools from another area of math called harmonic analysis might be useful.

“We do something that at first sight looks completely stupid,” said James Maynard of a mathematical gambit that he and a coauthor used to break a long-standing record.

Larry Guth, an expert in harmonic analysis who was at the same conference, just happened to already be thinking along similar lines. “But I didn’t know the analytic number theory at all well,” he said. Maynard explained the number theory side of the story to him over lunch and gave him a test case to work with. Guth studied it on and off for a few years, only to realize that his techniques from harmonic analysis wouldn’t work.

But he didn’t stop thinking about the problem, and he experimented with new approaches. He got back in touch with Maynard in February. The two started collaborating in earnest, combining their different perspectives. A few months later, they had their result.

A Mathematical Gambit

Guth and Maynard started out by converting the problem they wanted to solve into another one. If you have a zero that doesn’t have a real part of 1/2, then a related function, called a Dirichlet polynomial, must produce a very large output. As a result, proving that there are few exceptions to the Riemann hypothesis is equivalent to showing that the Dirichlet polynomial cannot get large too often.

Alex Kontorovich, professor of mathematics at Rutgers University, breaks down the notoriously difficult Riemann hypothesis in this comprehensive explainer.

The mathematicians then performed another act of translation. First, they used the Dirichlet polynomial to build a matrix, or a table of numbers. “Mathematicians love to see matrices, because matrices are one of the things that we understand really well,” Guth said. “You learn to keep your ears open and be ready to see that there are matrices all over the place.”

Matrices can “act on” a mathematical arrow called a vector, which is defined by a length and direction, to produce another vector. They generally change both the length and direction of the vector when they do so. Sometimes there are special vectors that, when run through a matrix, change only in length but not direction. These are called eigenvectors. Mathematicians measure the size of those changes using numbers called eigenvalues.

Guth and Maynard rewrote their problem so that it was now about the largest eigenvalue of their matrix. If they could show that the largest eigenvalue could not get too big, they’d be done. To do that, they used a formula that gave them a complicated sum, and searched for ways to make the positive and negative values in that sum cancel each other out as much as possible. “You have to rearrange the sequence, or look at it from the right angle, in order to see some symmetry that gives some cancellation,” Guth said.

That process involved several surprising steps, including “what’s in my mind the most important idea, which still seems a bit magical to me,” Maynard said. At one point, there was a seemingly obvious step they should have taken to simplify their sum. Instead, they left it in its longer and more complicated form. “We do something that at first sight looks completely stupid. We just refuse to do the standard simplification,” Maynard said. “And this gives up a lot. It means that now we can’t get any easy bound for this sum.”

But in the long run, this turned out to be an advantageous move. “In chess you call it a gambit, where you sacrifice a piece to get a better position on the board,” Maynard said. Guth likened it to playing with a Rubik’s Cube; sometimes you have to undo previous moves and make everything look worse before finding a way to get more colors in the right place.

Larry Guth’s expertise in harmonic analysis gave him a fresh perspective on a number theory problem that had resisted proof for decades.

“You have to be really brave to throw away an obvious improvement and hope that you can recover it later,” said Roger Heath-Brown , a mathematician at Oxford and Maynard’s former adviser. “That goes against everything that I thought you should be doing.”

In fact, he added of his own experiences working on this problem, “now that I think about it, that’s where I got stuck.”

Maynard said that Guth’s expertise as a harmonic analyst rather than a number theorist made this gambit possible. “He doesn’t inherently have these rules drilled into him, so he was more happy to consider things that go against the grain.”

Ultimately, they were able to get a good enough bound on the largest eigenvalue, which in turn translated to a better bound on the number of potential counterexamples to the Riemann hypothesis. Although their work began with the ideas from harmonic analysis that had inspired Guth, the mathematicians were ultimately able to cut those more complicated techniques out of the picture. “Now it looks exactly like the sort of thing I might have tried to do 40 years ago,” Heath-Brown said.

By giving a better bound on the number of zeros with a real part of 3/4, Guth and Maynard automatically proved results about how prime numbers are distributed. For example, estimates of how many primes are found in a given interval get less accurate for shorter intervals. The new work has allowed mathematicians to shorten the intervals in which they can get good estimates.

Mathematicians suspect that the proof will yield improvements to other statements about primes as well. There seems to be room to push Guth and Maynard’s techniques further. But “I feel that these aren’t the right techniques to solve the Riemann hypothesis itself,” Maynard said. “It’s going to need some big idea from somewhere else.”

Original story reprinted with permission from Quanta Magazine , an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences.

You Might Also Like …

In your inbox: Our biggest stories , handpicked for you each day

How one bad CrowdStrike update crashed the world’s computers

The Big Story: How soon might the Atlantic Ocean break ?

Welcome to the internet's hyper-consumption era

Olympics: Follow all our coverage from Paris this summer here