how to best critique a research paper

Making sense of research: A guide for critiquing a paper

Affiliation.

1 School of Nursing, Griffith University, Meadowbrook, Queensland.
PMID: 16114192
DOI: 10.5172/conu.14.1.38

Learning how to critique research articles is one of the fundamental skills of scholarship in any discipline. The range, quantity and quality of publications available today via print, electronic and Internet databases means it has become essential to equip students and practitioners with the prerequisites to judge the integrity and usefulness of published research. Finding, understanding and critiquing quality articles can be a difficult process. This article sets out some helpful indicators to assist the novice to make sense of research.

Publication types

Data Interpretation, Statistical
Research Design
Review Literature as Topic

IOE Writing Centre

Writing a Critical Review

Writing a Critique

A critique (or critical review) is not to be mistaken for a literature review. A 'critical review', or 'critique', is a complete type of text (or genre), discussing one particular article or book in detail. In some instances, you may be asked to write a critique of two or three articles (e.g. a comparative critical review). In contrast, a 'literature review', which also needs to be 'critical', is a part of a larger type of text, such as a chapter of your dissertation.

Most importantly: Read your article / book as many times as possible, as this will make the critical review much easier.

1. Read and take notes 2. Organising your writing 3. Summary 4. Evaluation 5. Linguistic features of a critical review 6. Summary language 7. Evaluation language 8. Conclusion language 9. Example extracts from a critical review 10. Further resources

Read and Take Notes

To improve your reading confidence and efficiency, visit our pages on reading.

Organising your writing

You first need to summarise the text that you have read. One reason to summarise the text is that the reader may not have read the text. In your summary, you will

focus on points within the article that you think are interesting
summarise the author(s) main ideas or argument
explain how these ideas / argument have been constructed. (For example, is the author basing her arguments on data that they have collected? Are the main ideas / argument purely theoretical?)

In your summary you might answer the following questions: Why is this topic important? Where can this text be located? For example, does it address policy studies? What other prominent authors also write about this?

Evaluation is the most important part in a critical review.

Use the literature to support your views. You may also use your knowledge of conducting research, and your own experience. Evaluation can be explicit or implicit.

Explicit evaluation

Explicit evaluation involves stating directly (explicitly) how you intend to evaluate the text. e.g. "I will review this article by focusing on the following questions. First, I will examine the extent to which the authors contribute to current thought on Second Language Acquisition (SLA) pedagogy. After that, I will analyse whether the authors' propositions are feasible within overseas SLA classrooms."

Implicit evaluation

Implicit evaluation is less direct. The following section on Linguistic Features of Writing a Critical Review contains language that evaluates the text. A difficult part of evaluation of a published text (and a professional author) is how to do this as a student. There is nothing wrong with making your position as a student explicit and incorporating it into your evaluation. Examples of how you might do this can be found in the section on Linguistic Features of Writing a Critical Review. You need to remember to locate and analyse the author's argument when you are writing your critical review. For example, you need to locate the authors' view of classroom pedagogy as presented in the book / article and not present a critique of views of classroom pedagogy in general.

Linguistic features of a critical review

The following examples come from published critical reviews. Some of them have been adapted for student use.

Summary language

This article / book is divided into two / three parts. First...
While the title might suggest...
The tone appears to be...
Title is the first / second volume in the series Title, edited by...The books / articles in this series address...
The second / third claim is based on...
The author challenges the notion that...
The author tries to find a more middle ground / make more modest claims...
The article / book begins with a short historical overview of...
Numerous authors have recently suggested that...(see Author, Year; Author, Year). Author would also be once such author. With his / her argument that...
To refer to title as a...is not to say that it is...
This book / article is aimed at... This intended readership...
The author's book / article examines the...To do this, the author first...
The author develops / suggests a theoretical / pedagogical model to…
This book / article positions itself firmly within the field of...
The author in a series of subtle arguments, indicates that he / she...
The argument is therefore...
The author asks "..."
With a purely critical / postmodern take on...
Topic, as the author points out, can be viewed as...
In this recent contribution to the field of...this British author...
As a leading author in the field of...
This book / article nicely contributes to the field of...and complements other work by this author...
The second / third part of...provides / questions / asks the reader...
Title is intended to encourage students / researchers to...
The approach taken by the author provides the opportunity to examine...in a qualitative / quantitative research framework that nicely complements...
The author notes / claims that state support / a focus on pedagogy / the adoption of...remains vital if...
According to Author (Year) teaching towards examinations is not as effective as it is in other areas of the curriculum. This is because, as Author (Year) claims that examinations have undue status within the curriculum.
According to Author (Year)…is not as effective in some areas of the curriculum / syllabus as others. Therefore the author believes that this is a reason for some school's…

Evaluation language

This argument is not entirely convincing, as...furthermore it commodifies / rationalises the...
Over the last five / ten years the view of...has increasingly been viewed as 'complicated' (see Author, Year; Author, Year).
However, through trying to integrate...with...the author...
There are difficulties with such a position.
Inevitably, several crucial questions are left unanswered / glossed over by this insightful / timely / interesting / stimulating book / article. Why should...
It might have been more relevant for the author to have written this book / article as...
This article / book is not without disappointment from those who would view...as...
This chosen framework enlightens / clouds...
This analysis intends to be...but falls a little short as...
The authors rightly conclude that if...
A detailed, well-written and rigorous account of...
As a Korean student I feel that this article / book very clearly illustrates...
The beginning of...provides an informative overview into...
The tables / figures do little to help / greatly help the reader...
The reaction by scholars who take a...approach might not be so favourable (e.g. Author, Year).
This explanation has a few weaknesses that other researchers have pointed out (see Author, Year; Author, Year). The first is...
On the other hand, the author wisely suggests / proposes that...By combining these two dimensions...
The author's brief introduction to...may leave the intended reader confused as it fails to properly...
Despite my inability to...I was greatly interested in...
Even where this reader / I disagree(s), the author's effort to...
The author thus combines...with...to argue...which seems quite improbable for a number of reasons. First...
Perhaps this aversion to...would explain the author's reluctance to...
As a second language student from ...I find it slightly ironic that such an anglo-centric view is...
The reader is rewarded with...
Less convincing is the broad-sweeping generalisation that...
There is no denying the author's subject knowledge nor his / her...
The author's prose is dense and littered with unnecessary jargon...
The author's critique of...might seem harsh but is well supported within the literature (see Author, Year; Author, Year; Author, Year). Aligning herself with the author, Author (Year) states that...
As it stands, the central focus of Title is well / poorly supported by its empirical findings...
Given the hesitation to generalise to...the limitation of...does not seem problematic...
For instance, the term...is never properly defined and the reader left to guess as to whether...
Furthermore, to label...as...inadvertently misguides...
In addition, this research proves to be timely / especially significant to... as recent government policy / proposals has / have been enacted to...
On this well researched / documented basis the author emphasises / proposes that...
Nonetheless, other research / scholarship / data tend to counter / contradict this possible trend / assumption...(see Author, Year; Author, Year).
Without entering into detail of the..., it should be stated that Title should be read by...others will see little value in...
As experimental conditions were not used in the study the word 'significant' misleads the reader.
The article / book becomes repetitious in its assertion that...
The thread of the author's argument becomes lost in an overuse of empirical data...
Almost every argument presented in the final section is largely derivative, providing little to say about...
She / he does not seem to take into consideration; however, that there are fundamental differences in the conditions of…
As Author (Year) points out, however, it seems to be necessary to look at…
This suggest that having low…does not necessarily indicate that…is ineffective.
Therefore, the suggestion made by Author (Year)…is difficult to support.
When considering all the data presented…it is not clear that the low scores of some students, indeed, reflects…

Conclusion language

Overall this article / book is an analytical look at...which within the field of...is often overlooked.
Despite its problems, Title offers valuable theoretical insights / interesting examples / a contribution to pedagogy and a starting point for students / researchers of...with an interest in...
This detailed and rigorously argued...
This first / second volume / book / article by...with an interest in...is highly informative...

Example extracts from a critical review

Writing critically.

If you have been told your writing is not critical enough, it probably means that your writing treats the knowledge claims as if they are true, well supported, and applicable in the context you are writing about. This may not always be the case.

In these two examples, the extracts refer to the same section of text. In each example, the section that refers to a source has been highlighted in bold. The note below the example then explains how the writer has used the source material.

There is a strong positive effect on students, both educationally and emotionally, when the instructors try to learn to say students' names without making pronunciation errors (Kiang, 2004).

Use of source material in example a:

This is a simple paraphrase with no critical comment. It looks like the writer agrees with Kiang. (This is not a good example for critical writing, as the writer has not made any critical comment).

Kiang (2004) gives various examples to support his claim that "the positive emotional and educational impact on students is clear" (p.210) when instructors try to pronounce students' names in the correct way. He quotes one student, Nguyet, as saying that he "felt surprised and happy" (p.211) when the tutor said his name clearly . The emotional effect claimed by Kiang is illustrated in quotes such as these, although the educational impact is supported more indirectly through the chapter. Overall, he provides more examples of students being negatively affected by incorrect pronunciation, and it is difficult to find examples within the text of a positive educational impact as such.

Use of source material in example b:

The writer describes Kiang's (2004) claim and the examples which he uses to try to support it. The writer then comments that the examples do not seem balanced and may not be enough to support the claims fully. This is a better example of writing which expresses criticality.

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Further resources

You may also be interested in our page on criticality, which covers criticality in general, and includes more critical reading questions.

Further reading: Read and Write Critically

We recommend that you do not search for other university guidelines on critical reviews. This is because the expectations may be different at other institutions. Ask your tutor for more guidance or examples if you have further questions.

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A critique asks you to evaluate an article and the author’s argument. You will need to look critically at what the author is claiming, evaluate the research methods, and look for possible problems with, or applications of, the researcher’s claims.

Introduction

Give an overview of the author’s main points and how the author supports those points. Explain what the author found and describe the process they used to arrive at this conclusion.

Body Paragraphs

Interpret the information from the article:

Does the author review previous studies? Is current and relevant research used?
What type of research was used – empirical studies, anecdotal material, or personal observations?
Was the sample too small to generalize from?
Was the participant group lacking in diversity (race, gender, age, education, socioeconomic status, etc.)
For instance, volunteers gathered at a health food store might have different attitudes about nutrition than the population at large.
How useful does this work seem to you? How does the author suggest the findings could be applied and how do you believe they could be applied?
How could the study have been improved in your opinion?
Does the author appear to have any biases (related to gender, race, class, or politics)?
Is the writing clear and easy to follow? Does the author’s tone add to or detract from the article?
How useful are the visuals (such as tables, charts, maps, photographs) included, if any? How do they help to illustrate the argument? Are they confusing or hard to read?
What further research might be conducted on this subject?

Try to synthesize the pieces of your critique to emphasize your own main points about the author’s work, relating the researcher’s work to your own knowledge or to topics being discussed in your course.

From the Center for Academic Excellence (opens in a new window), University of Saint Joseph Connecticut

Additional Resources

All links open in a new window.

Writing an Article Critique (from The University of Arizona Global Campus Writing Center)

How to Critique an Article (from Essaypro.com)

How to Write an Article Critique (from EliteEditing.com.au)

<< Previous: Writing an article REVIEW
Next: Citing Sources >>
Last Updated: Mar 15, 2024 9:32 AM
URL: https://libguides.randolph.edu/summaries

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Methodology

How to Write a Literature Review | Guide, Examples, & Templates

How to Write a Literature Review | Guide, Examples, & Templates

Published on January 2, 2023 by Shona McCombes . Revised on September 11, 2023.

What is a literature review? A literature review is a survey of scholarly sources on a specific topic. It provides an overview of current knowledge, allowing you to identify relevant theories, methods, and gaps in the existing research that you can later apply to your paper, thesis, or dissertation topic .

There are five key steps to writing a literature review:

Search for relevant literature
Evaluate sources
Identify themes, debates, and gaps
Outline the structure
Write your literature review

A good literature review doesn’t just summarize sources—it analyzes, synthesizes , and critically evaluates to give a clear picture of the state of knowledge on the subject.

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What is the purpose of a literature review, examples of literature reviews, step 1 – search for relevant literature, step 2 – evaluate and select sources, step 3 – identify themes, debates, and gaps, step 4 – outline your literature review’s structure, step 5 – write your literature review, free lecture slides, other interesting articles, frequently asked questions, introduction.

Quick Run-through
Step 1 & 2

When you write a thesis , dissertation , or research paper , you will likely have to conduct a literature review to situate your research within existing knowledge. The literature review gives you a chance to:

Demonstrate your familiarity with the topic and its scholarly context
Develop a theoretical framework and methodology for your research
Position your work in relation to other researchers and theorists
Show how your research addresses a gap or contributes to a debate
Evaluate the current state of research and demonstrate your knowledge of the scholarly debates around your topic.

Writing literature reviews is a particularly important skill if you want to apply for graduate school or pursue a career in research. We’ve written a step-by-step guide that you can follow below.

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Writing literature reviews can be quite challenging! A good starting point could be to look at some examples, depending on what kind of literature review you’d like to write.

Example literature review #1: “Why Do People Migrate? A Review of the Theoretical Literature” ( Theoretical literature review about the development of economic migration theory from the 1950s to today.)
Example literature review #2: “Literature review as a research methodology: An overview and guidelines” ( Methodological literature review about interdisciplinary knowledge acquisition and production.)
Example literature review #3: “The Use of Technology in English Language Learning: A Literature Review” ( Thematic literature review about the effects of technology on language acquisition.)
Example literature review #4: “Learners’ Listening Comprehension Difficulties in English Language Learning: A Literature Review” ( Chronological literature review about how the concept of listening skills has changed over time.)

You can also check out our templates with literature review examples and sample outlines at the links below.

Download Word doc Download Google doc

Before you begin searching for literature, you need a clearly defined topic .

If you are writing the literature review section of a dissertation or research paper, you will search for literature related to your research problem and questions .

Make a list of keywords

Start by creating a list of keywords related to your research question. Include each of the key concepts or variables you’re interested in, and list any synonyms and related terms. You can add to this list as you discover new keywords in the process of your literature search.

Social media, Facebook, Instagram, Twitter, Snapchat, TikTok
Body image, self-perception, self-esteem, mental health
Generation Z, teenagers, adolescents, youth

Search for relevant sources

Use your keywords to begin searching for sources. Some useful databases to search for journals and articles include:

Your university’s library catalogue
Google Scholar
Project Muse (humanities and social sciences)
Medline (life sciences and biomedicine)
EconLit (economics)
Inspec (physics, engineering and computer science)

You can also use boolean operators to help narrow down your search.

Make sure to read the abstract to find out whether an article is relevant to your question. When you find a useful book or article, you can check the bibliography to find other relevant sources.

You likely won’t be able to read absolutely everything that has been written on your topic, so it will be necessary to evaluate which sources are most relevant to your research question.

For each publication, ask yourself:

What question or problem is the author addressing?
What are the key concepts and how are they defined?
What are the key theories, models, and methods?
Does the research use established frameworks or take an innovative approach?
What are the results and conclusions of the study?
How does the publication relate to other literature in the field? Does it confirm, add to, or challenge established knowledge?
What are the strengths and weaknesses of the research?

Make sure the sources you use are credible , and make sure you read any landmark studies and major theories in your field of research.

You can use our template to summarize and evaluate sources you’re thinking about using. Click on either button below to download.

Take notes and cite your sources

As you read, you should also begin the writing process. Take notes that you can later incorporate into the text of your literature review.

It is important to keep track of your sources with citations to avoid plagiarism . It can be helpful to make an annotated bibliography , where you compile full citation information and write a paragraph of summary and analysis for each source. This helps you remember what you read and saves time later in the process.

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

Academic style
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See an example

To begin organizing your literature review’s argument and structure, be sure you understand the connections and relationships between the sources you’ve read. Based on your reading and notes, you can look for:

Trends and patterns (in theory, method or results): do certain approaches become more or less popular over time?
Themes: what questions or concepts recur across the literature?
Debates, conflicts and contradictions: where do sources disagree?
Pivotal publications: are there any influential theories or studies that changed the direction of the field?
Gaps: what is missing from the literature? Are there weaknesses that need to be addressed?

This step will help you work out the structure of your literature review and (if applicable) show how your own research will contribute to existing knowledge.

Most research has focused on young women.
There is an increasing interest in the visual aspects of social media.
But there is still a lack of robust research on highly visual platforms like Instagram and Snapchat—this is a gap that you could address in your own research.

There are various approaches to organizing the body of a literature review. Depending on the length of your literature review, you can combine several of these strategies (for example, your overall structure might be thematic, but each theme is discussed chronologically).

Chronological

The simplest approach is to trace the development of the topic over time. However, if you choose this strategy, be careful to avoid simply listing and summarizing sources in order.

Try to analyze patterns, turning points and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred.

If you have found some recurring central themes, you can organize your literature review into subsections that address different aspects of the topic.

For example, if you are reviewing literature about inequalities in migrant health outcomes, key themes might include healthcare policy, language barriers, cultural attitudes, legal status, and economic access.

Methodological

If you draw your sources from different disciplines or fields that use a variety of research methods , you might want to compare the results and conclusions that emerge from different approaches. For example:

Look at what results have emerged in qualitative versus quantitative research
Discuss how the topic has been approached by empirical versus theoretical scholarship
Divide the literature into sociological, historical, and cultural sources

Theoretical

A literature review is often the foundation for a theoretical framework . You can use it to discuss various theories, models, and definitions of key concepts.

You might argue for the relevance of a specific theoretical approach, or combine various theoretical concepts to create a framework for your research.

Like any other academic text , your literature review should have an introduction , a main body, and a conclusion . What you include in each depends on the objective of your literature review.

The introduction should clearly establish the focus and purpose of the literature review.

Depending on the length of your literature review, you might want to divide the body into subsections. You can use a subheading for each theme, time period, or methodological approach.

As you write, you can follow these tips:

Summarize and synthesize: give an overview of the main points of each source and combine them into a coherent whole
Analyze and interpret: don’t just paraphrase other researchers — add your own interpretations where possible, discussing the significance of findings in relation to the literature as a whole
Critically evaluate: mention the strengths and weaknesses of your sources
Write in well-structured paragraphs: use transition words and topic sentences to draw connections, comparisons and contrasts

In the conclusion, you should summarize the key findings you have taken from the literature and emphasize their significance.

When you’ve finished writing and revising your literature review, don’t forget to proofread thoroughly before submitting. Not a language expert? Check out Scribbr’s professional proofreading services !

This article has been adapted into lecture slides that you can use to teach your students about writing a literature review.

Scribbr slides are free to use, customize, and distribute for educational purposes.

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

Sampling methods
Simple random sampling
Stratified sampling
Cluster sampling
Likert scales
Reproducibility

Statistics

Null hypothesis
Statistical power
Probability distribution
Effect size
Poisson distribution

Research bias

Optimism bias
Cognitive bias
Implicit bias
Hawthorne effect
Anchoring bias
Explicit bias

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a thesis, dissertation , or research paper , in order to situate your work in relation to existing knowledge.

There are several reasons to conduct a literature review at the beginning of a research project:

To familiarize yourself with the current state of knowledge on your topic
To ensure that you’re not just repeating what others have already done
To identify gaps in knowledge and unresolved problems that your research can address
To develop your theoretical framework and methodology
To provide an overview of the key findings and debates on the topic

Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.

The literature review usually comes near the beginning of your thesis or dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .

A literature review is a survey of credible sources on a topic, often used in dissertations , theses, and research papers . Literature reviews give an overview of knowledge on a subject, helping you identify relevant theories and methods, as well as gaps in existing research. Literature reviews are set up similarly to other academic texts , with an introduction , a main body, and a conclusion .

An annotated bibliography is a list of source references that has a short description (called an annotation ) for each of the sources. It is often assigned as part of the research process for a paper .

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The Writing Center • University of North Carolina at Chapel Hill

Writing Critiques

Writing a critique involves more than pointing out mistakes. It involves conducting a systematic analysis of a scholarly article or book and then writing a fair and reasonable description of its strengths and weaknesses. Several scholarly journals have published guides for critiquing other people’s work in their academic area. Search for a “manuscript reviewer guide” in your own discipline to guide your analysis of the content. Use this handout as an orientation to the audience and purpose of different types of critiques and to the linguistic strategies appropriate to all of them.

Types of critique

Article or book review assignment in an academic class.

Text: Article or book that has already been published Audience: Professors Purpose:

to demonstrate your skills for close reading and analysis
to show that you understand key concepts in your field
to learn how to review a manuscript for your future professional work

Published book review

Text: Book that has already been published Audience: Disciplinary colleagues Purpose:

to describe the book’s contents
to summarize the book’s strengths and weaknesses
to provide a reliable recommendation to read (or not read) the book

Manuscript review

Text: Manuscript that has been submitted but has not been published yet Audience: Journal editor and manuscript authors Purpose:

to provide the editor with an evaluation of the manuscript
to recommend to the editor that the article be published, revised, or rejected
to provide the authors with constructive feedback and reasonable suggestions for revision

Language strategies for critiquing

For each type of critique, it’s important to state your praise, criticism, and suggestions politely, but with the appropriate level of strength. The following language structures should help you achieve this challenging task.

Offering Praise and Criticism

A strategy called “hedging” will help you express praise or criticism with varying levels of strength. It will also help you express varying levels of certainty in your own assertions. Grammatical structures used for hedging include:

Modal verbs Using modal verbs (could, can, may, might, etc.) allows you to soften an absolute statement. Compare:

This text is inappropriate for graduate students who are new to the field. This text may be inappropriate for graduate students who are new to the field.

Qualifying adjectives and adverbs Using qualifying adjectives and adverbs (possible, likely, possibly, somewhat, etc.) allows you to introduce a level of probability into your comments. Compare:

Readers will find the theoretical model difficult to understand. Some readers will find the theoretical model difficult to understand. Some readers will probably find the theoretical model somewhat difficult to understand completely.

Note: You can see from the last example that too many qualifiers makes the idea sound undesirably weak.

Tentative verbs Using tentative verbs (seems, indicates, suggests, etc.) also allows you to soften an absolute statement. Compare:

This omission shows that the authors are not aware of the current literature. This omission indicates that the authors are not aware of the current literature. This omission seems to suggest that the authors are not aware of the current literature.

Offering suggestions

Whether you are critiquing a published or unpublished text, you are expected to point out problems and suggest solutions. If you are critiquing an unpublished manuscript, the author can use your suggestions to revise. Your suggestions have the potential to become real actions. If you are critiquing a published text, the author cannot revise, so your suggestions are purely hypothetical. These two situations require slightly different grammar.

Unpublished manuscripts: “would be X if they did Y” Reviewers commonly point out weakness by pointing toward improvement. For instance, if the problem is “unclear methodology,” reviewers may write that “the methodology would be more clear if …” plus a suggestion. If the author can use the suggestions to revise, the grammar is “X would be better if the authors did Y” (would be + simple past suggestion).

The tables would be clearer if the authors highlighted the key results. The discussion would be more persuasive if the authors accounted for the discrepancies in the data.

Published manuscripts: “would have been X if they had done Y” If the authors cannot revise based on your suggestions, use the past unreal conditional form “X would have been better if the authors had done Y” (would have been + past perfect suggestion).

The tables would have been clearer if the authors had highlighted key results. The discussion would have been more persuasive if the authors had accounted for discrepancies in the data.

Note: For more information on conditional structures, see our Conditionals handout .

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How to Write an Article Critique

Tips for Writing a Psychology Critique Paper

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

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

Cultura RM / Gu Cultura / Getty Images

Steps for Writing a Critique

Evaluating the Article

How to Write It
Helpful Tips

An article critique involves critically analyzing a written work to assess its strengths and flaws. If you need to write an article critique, you will need to describe the article, analyze its contents, interpret its meaning, and make an overall assessment of the importance of the work.

Critique papers require students to conduct a critical analysis of another piece of writing, often a book, journal article, or essay . No matter your major, you will probably be expected to write a critique paper at some point.

For psychology students, critiquing a professional paper is a great way to learn more about psychology articles, writing, and the research process itself. Students will analyze how researchers conduct experiments, interpret results, and discuss the impact of the results.

At a Glance

An article critique involves making a critical assessment of a single work. This is often an article, but it might also be a book or other written source. It summarizes the contents of the article and then evaluates both the strengths and weaknesses of the piece. Knowing how to write an article critique can help you learn how to evaluate sources with a discerning eye.

Steps for Writing an Effective Article Critique

While these tips are designed to help students write a psychology critique paper, many of the same principles apply to writing article critiques in other subject areas.

Your first step should always be a thorough read-through of the material you will be analyzing and critiquing. It needs to be more than just a casual skim read. It should be in-depth with an eye toward key elements.

To write an article critique, you should:

Read the article , noting your first impressions, questions, thoughts, and observations
Describe the contents of the article in your own words, focusing on the main themes or ideas
Interpret the meaning of the article and its overall importance
Critically evaluate the contents of the article, including any strong points as well as potential weaknesses

The following guidelines can help you assess the article you are reading and make better sense of the material.

Read the Introduction Section of the Article

Start by reading the introduction . Think about how this part of the article sets up the main body and how it helps you get a background on the topic.

Is the hypothesis clearly stated?
Is the necessary background information and previous research described in the introduction?

In addition to answering these basic questions, note other information provided in the introduction and any questions you have.

Read the Methods Section of the Article

Is the study procedure clearly outlined in the methods section ? Can you determine which variables the researchers are measuring?

Remember to jot down questions and thoughts that come to mind as you are reading. Once you have finished reading the paper, you can then refer back to your initial questions and see which ones remain unanswered.

Read the Results Section of the Article

Are all tables and graphs clearly labeled in the results section ? Do researchers provide enough statistical information? Did the researchers collect all of the data needed to measure the variables in question?

Make a note of any questions or information that does not seem to make sense. You can refer back to these questions later as you are writing your final critique.

Read the Discussion Section of the Article

Experts suggest that it is helpful to take notes while reading through sections of the paper you are evaluating. Ask yourself key questions:

How do the researchers interpret the results of the study?
Did the results support their hypothesis?
Do the conclusions drawn by the researchers seem reasonable?

The discussion section offers students an excellent opportunity to take a position. If you agree with the researcher's conclusions, explain why. If you feel the researchers are incorrect or off-base, point out problems with the conclusions and suggest alternative explanations.

Another alternative is to point out questions the researchers failed to answer in the discussion section.

Begin Writing Your Own Critique of the Paper

Once you have read the article, compile your notes and develop an outline that you can follow as you write your psychology critique paper. Here's a guide that will walk you through how to structure your critique paper.

Introduction

Begin your paper by describing the journal article and authors you are critiquing. Provide the main hypothesis (or thesis) of the paper. Explain why you think the information is relevant.

Thesis Statement

The final part of your introduction should include your thesis statement. Your thesis statement is the main idea of your critique. Your thesis should briefly sum up the main points of your critique.

Article Summary

Provide a brief summary of the article. Outline the main points, results, and discussion.

When describing the study or paper, experts suggest that you include a summary of the questions being addressed, study participants, interventions, comparisons, outcomes, and study design.

Don't get bogged down by your summary. This section should highlight the main points of the article you are critiquing. Don't feel obligated to summarize each little detail of the main paper. Focus on giving the reader an overall idea of the article's content.

Your Analysis

In this section, you will provide your critique of the article. Describe any problems you had with the author's premise, methods, or conclusions. You might focus your critique on problems with the author's argument, presentation, information, and alternatives that have been overlooked.

When evaluating a study, summarize the main findings—including the strength of evidence for each main outcome—and consider their relevance to key demographic groups.

Organize your paper carefully. Be careful not to jump around from one argument to the next. Arguing one point at a time ensures that your paper flows well and is easy to read.

Your critique paper should end with an overview of the article's argument, your conclusions, and your reactions.

What This Means For You

Being able to write a solid article critique is a useful academic skill. While it can be challenging, start by breaking down the sections of the paper, noting your initial thoughts and questions. Then structure your own critique so that you present a summary followed by your evaluation. In your critique, include the strengths and the weaknesses of the article.

Archibald D, Martimianakis MA. Writing, reading, and critiquing reviews . Can Med Educ J . 2021;12(3):1-7. doi:10.36834/cmej.72945

Pautasso M. Ten simple rules for writing a literature review . PLoS Comput Biol . 2013;9(7):e1003149. doi:10.1371/journal.pcbi.1003149

Gülpınar Ö, Güçlü AG. How to write a review article? Turk J Urol . 2013;39(Suppl 1):44–48. doi:10.5152/tud.2013.054

Erol A. Basics of writing review articles . Noro Psikiyatr Ars . 2022;59(1):1-2. doi:10.29399/npa.28093

American Psychological Association. Publication Manual of the American Psychological Association (7th ed.). Washington DC: The American Psychological Association; 2019.

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

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A guide to critiquing a research paper. Methodological appraisal of a paper on nurses in abortion care (Lipp & Fothergill)
Step-by-step guide to critiquing research. Part 1: Quantitative research (Coughlan et al.)
Step-by-step guide to critiquing research. Part 2: Qualitative research (Coughlan et al.)

Guidelines:

Critiquing Research Articles (Flinders University)
Framework for How to Read and Critique a Research Study (American Nurses Association)
How to Critique a Journal Article (UIS)
How to Critique a Research Paper (University of Michigan)
How to Write an Article Critique
Research Article Critique Form
Writing a Critique or Review of a Research Article (University of Calgary)

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The Critique Process: Reviewing and Critiquing Research
Writing a Critique
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Writing well

How to write a critique

Starting well
How to write an annotated bibliography
How to write a case study response
How to write an empirical article
How to write an essay
How to write a literature review
How to write a reflective task
How to write a report
Finishing well

Before you start writing, it is important to have a thorough understanding of the work that will be critiqued.

Study the work under discussion.
Make notes on key parts of the work.
Develop an understanding of the main argument or purpose being expressed in the work.
Consider how the work relates to a broader issue or context.

Example template

There are a variety of ways to structure a critique. You should always check your unit materials or Canvas site for guidance from your lecturer. The following template, which showcases the main features of a critique, is provided as one example.

Introduction

Typically, the introduction is short (less than 10% of the word length) and you should:

name the work being reviewed as well as the date it was created and the name of the author/creator
describe the main argument or purpose of the work
explain the context in which the work was created - this could include the social or political context, the place of the work in a creative or academic tradition, or the relationship between the work and the creator’s life experience
have a concluding sentence that signposts what your evaluation of the work will be - for instance, it may indicate whether it is a positive, negative, or mixed evaluation.

Briefly summarise the main points and objectively describe how the creator portrays these by using techniques, styles, media, characters or symbols. This summary should not be the focus of the critique and is usually shorter than the critical evaluation.

Critical evaluation

This section should give a systematic and detailed assessment of the different elements of the work, evaluating how well the creator was able to achieve the purpose through these. For example: you would assess the plot structure, characterisation and setting of a novel; an assessment of a painting would look at composition, brush strokes, colour and light; a critique of a research project would look at subject selection, design of the experiment, analysis of data and conclusions.

A critical evaluation does not simply highlight negative impressions. It should deconstruct the work and identify both strengths and weaknesses. It should examine the work and evaluate its success, in light of its purpose.

Examples of key critical questions that could help your assessment include:

Who is the creator? Is the work presented objectively or subjectively?
What are the aims of the work? Were the aims achieved?
What techniques, styles, media were used in the work? Are they effective in portraying the purpose?
What assumptions underlie the work? Do they affect its validity?
What types of evidence or persuasion are used? Has evidence been interpreted fairly?
How is the work structured? Does it favour a particular interpretation or point of view? Is it effective?
Does the work enhance understanding of key ideas or theories? Does the work engage (or fail to engage) with key concepts or other works in its discipline?

This evaluation is written in formal academic style and logically presented. Group and order your ideas into paragraphs. Start with the broad impressions first and then move into the details of the technical elements. For shorter critiques, you may discuss the strengths of the works, and then the weaknesses. In longer critiques, you may wish to discuss the positive and negative of each key critical question in individual paragraphs.

To support the evaluation, provide evidence from the work itself, such as a quote or example, and you should also cite evidence from related sources. Explain how this evidence supports your evaluation of the work.

This is usually a very brief paragraph, which includes:

a statement indicating the overall evaluation of the work
a summary of the key reasons, identified during the critical evaluation, why this evaluation was formed
in some circumstances, recommendations for improvement on the work may be appropriate.

Reference list

Include all resources cited in your critique. Check with your lecturer/tutor for which referencing style to use.

Mentioned the name of the work, the date of its creation and the name of the creator?
Accurately summarised the work being critiqued?
Mainly focused on the critical evaluation of the work?
Systematically outlined an evaluation of each element of the work to achieve the overall purpose?
Used evidence, from the work itself as well as other sources, to back and illustrate my assessment of elements of the work?
Formed an overall evaluation of the work, based on critical reading?
Used a well structured introduction, body and conclusion?
Used correct grammar, spelling and punctuation; clear presentation; and appropriate referencing style?

Further information

University of New South Wales: Writing a Critical Review
University of Toronto: The Book Review or Article Critique

Acknowledgement of Traditional Owners

QUT acknowledges the Traditional Owners of the lands where QUT now stands.

Spartanburg Community College Library
SCC Research Guides
Writing a Critique
Parts of a Critique

Depending on the source you are critiquing, your critique may not follow this exact format below. However, in general, critiques will be formatted in a similar way.

Introduction

The name of the source or event
What kind of source it is (book, film, lecture, etc.)
The name of the author or the speaker
The author or speaker's experience/expertise on the topic
The main argument in the source (or the thesis statement of the source)
The intended (target) audience for the source or event
The purpose of the source or the event
Did the author/speaker well-support their thesis statement?
Did the author use any interest supports (stories, humor, examples, interactions, personal experience, etc.). Were they effective?
What kind of evidence did the author/speaker use in the source (statistics, facts, quotations, surveys, studies, interviews, expert opinions). Are these resources credible/reliable? Did the evidence add to or contradict the author/speaker's argument?
Did the source have quality content (avoiding fillers, presented newsworthy information, kept audiences interested)?
Did the source use any visual aids (PowerPoint, images, artwork, etc.). Did the visual aids match or enhance what the author/speaker was discussing? Were the visual aids clearly organized, spell-checked, and included citations?
Did the speaker move well through different topics?
If the source was a live event or a recording, was the speaker energetic? Did they talk to the crowd or did they look at their notes too much? Were you able to hear and understand the speaker?
If you're critiquing a film, were the film techniques used effective?

Conclusion/Recommendation

What was your overall impression of the source?
Would you recommend this source to others? Why or why not?
What are your final thoughts about the source?

Helpful Handouts

Sample Critique Paper Check out a sample critique essay of an event a student attended.
How to Critique for a Live Performance (WOW) This worksheet will provide an outline of how to write a critique for the Wonders of Writing (WOW) event at SCC.
How to Critique a Live/Zoom Presentation (Informational Presentation) This worksheet will show the outline for writing a critique for a live or a Zoom informational presentation.
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How to Critique a Research Article

Published: 01 October 2023

Let's briefly examine some basic pointers on how to perform a literature review.

If you've managed to get your hands on peer-reviewed articles, then you may wonder why it is necessary for you to perform your own article critique. Surely the article will be of good quality if it has made it through the peer-review process?

Unfortunately, this is not always the case.

Publication bias can occur when editors only accept manuscripts that have a bearing on the direction of their own research, or reject manuscripts with negative findings. Additionally, not all peer reviewers have expert knowledge on certain subject matters , which can introduce bias and sometimes a conflict of interest.

Performing your own critical analysis of an article allows you to consider its value to you and to your workplace.

Critical evaluation is defined as a systematic way of considering the truthfulness of a piece of research, its results and how relevant and applicable they are.

How to Critique

It can be a little overwhelming trying to critique an article when you're not sure where to start. Considering the article under the following headings may be of some use:

Title of Study/Research

You may be a better judge of this after reading the article, but the title should succinctly reflect the content of the work, stimulating readers' interest.

Three to six keywords that encapsulate the main topics of the research will have been drawn from the body of the article.

Introduction

This should include:

Evidence of a literature review that is relevant and recent, critically appraising other works rather than merely describing them
Background information on the study to orientate the reader to the problem
Hypothesis or aims of the study
Rationale for the study that justifies its need, i.e. to explore an un-investigated gap in the literature.

Materials and Methods

Similar to a recipe, the description of materials and methods will allow others to replicate the study elsewhere if needed. It should both contain and justify the exact specifications of selection criteria, sample size, response rate and any statistics used. This will demonstrate how the study is capable of achieving its aims. Things to consider in this section are:

What sort of sampling technique and size was used?
What proportion of the eligible sample participated? (e.g. '553 responded to a survey sent to 750 medical technologists'
Were all eligible groups sampled? (e.g. was the survey sent only in English?)
What were the strengths and weaknesses of the study?
Were there threats to the reliability and validity of the study, and were these controlled for?
Were there any obvious biases?
If a trial was undertaken, was it randomised, case-controlled, blinded or double-blinded?

Results should be statistically analysed and presented in a way that an average reader of the journal will understand. Graphs and tables should be clear and promote clarity of the text. Consider whether:

There were any major omissions in the results, which could indicate bias
Percentages have been used to disguise small sample sizes
The data generated is consistent with the data collected.

Negative results are just as relevant as research that produces positive results (but, as mentioned previously, may be omitted in publication due to editorial bias).

This should show insight into the meaning and significance of the research findings. It should not introduce any new material but should address how the aims of the study have been met. The discussion should use previous research work and theoretical concepts as the context in which the new study can be interpreted. Any limitations of the study, including bias, should be clearly presented. You will need to evaluate whether the author has clearly interpreted the results of the study, or whether the results could be interpreted another way.

Conclusions

These should be clearly stated and will only be valid if the study was reliable, valid and used a representative sample size. There may also be recommendations for further research.

These should be relevant to the study, be up-to-date, and should provide a comprehensive list of citations within the text.

Final Thoughts

Undertaking a critique of a research article may seem challenging at first, but will help you to evaluate whether the article has relevance to your own practice and workplace. Reading a single article can act as a springboard into researching the topic more widely, and aids in ensuring your nursing practice remains current and is supported by existing literature.

Marshall, G 2005, ‘Critiquing a Research Article’, Radiography , vol. 11, no. 1, viewed 2 October 2023, https://www.radiographyonline.com/article/S1078-8174(04)00119-1/fulltext

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Writing a Critique

About this Guide
What Is a Critique?
Getting Started
Components of a Critique Essay

Types of Critiques

There are many types of critiques. Critiques can be written on:

Literary works
Published works
Drafts of works
Policies, of any kind
Works of art

Anywhere that criticism can exist, a critique can follow to evaluate arguments, identify gaps, and/or make recommendations.

Defining Critique

A critique evaluates a resource. It requires both critical reading and analysis in order to present the strengths and weaknesses of a particular resource for readers. The critique includes your opinion of the work. Because of the analytics involved, a critique and a summary are not the same. For quick reference, you can use the following chart in order to determine if your paper is a critique or a summary.

Looking for more information on writing a summary or an abstract? Check out our Writing a Summary guide .

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Next: Getting Started >>
Last Updated: May 22, 2023 10:46 AM
URL: https://library.tiffin.edu/critique

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To critique a piece of writing is to do the following:

describe: give the reader a sense of the writer’s overall purpose and intent
analyze: examine how the structure and language of the text convey its meaning
interpret: state the significance or importance of each part of the text
assess: make a judgment of the work’s worth or value

FORMATTING A CRITIQUE

Here are two structures for critiques, one for nonfiction and one for fiction/literature.

The Critique Format for Nonfiction

Introduction

name of author and work
general overview of subject and summary of author's argument
focusing (or thesis) sentence indicating how you will divide the whole work for discussion or the particular elements you will discuss
objective description of a major point in the work
detailed analysis of how the work conveys an idea or concept
interpretation of the concept
repetition of description, analysis, interpretation if more than one major concept is covered
overall interpretation
relationship of particular interpretations to subject as a whole
critical assessment of the value, worth, or meaning of the work, both negative and positive

The Critique Format for Fiction/Literature

brief summary/description of work as a whole
focusing sentence indicating what element you plan to examine
general indication of overall significance of work
literal description of the first major element or portion of the work
detailed analysis
interpretation
literal description of second major element
interpretation (including, if necessary, the relationship to the first major point)
overall interpretation of the elements studied
consideration of those elements within the context of the work as a whole
critical assessment of the value, worth, meaning, or significance of the work, both positive and negative

You may not be asked in every critique to assess a work, only to analyze and interpret it. If you are asked for a personal response, remember that your assessment should not be the expression of an unsupported personal opinion. Your interpretations and your conclusions must be based on evidence from the text and follow from the ideas you have dealt with in the paper.

Remember also that a critique may express a positive as well as a negative assessment. Don't confuse critique with criticize in the popular sense of the word, meaning “to point out faults.”

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Writing a Scientific Review Article: Comprehensive Insights for Beginners

Ayodeji amobonye.

1 Department of Biotechnology and Food Science, Faculty of Applied Sciences, Durban University of Technology, P.O. Box 1334, KwaZulu-Natal, Durban 4000, South Africa

2 Writing Centre, Durban University of Technology, P.O. Box 1334 KwaZulu-Natal, Durban 4000, South Africa

Japareng Lalung

3 School of Industrial Technology, Universiti Sains Malaysia, Gelugor 11800, Pulau Pinang, Malaysia

Santhosh Pillai

Associated data.

The data and materials that support the findings of this study are available from the corresponding author upon reasonable request.

Review articles present comprehensive overview of relevant literature on specific themes and synthesise the studies related to these themes, with the aim of strengthening the foundation of knowledge and facilitating theory development. The significance of review articles in science is immeasurable as both students and researchers rely on these articles as the starting point for their research. Interestingly, many postgraduate students are expected to write review articles for journal publications as a way of demonstrating their ability to contribute to new knowledge in their respective fields. However, there is no comprehensive instructional framework to guide them on how to analyse and synthesise the literature in their niches into publishable review articles. The dearth of ample guidance or explicit training results in students having to learn all by themselves, usually by trial and error, which often leads to high rejection rates from publishing houses. Therefore, this article seeks to identify these challenges from a beginner's perspective and strives to plug the identified gaps and discrepancies. Thus, the purpose of this paper is to serve as a systematic guide for emerging scientists and to summarise the most important information on how to write and structure a publishable review article.

1. Introduction

Early scientists, spanning from the Ancient Egyptian civilization to the Scientific Revolution of the 16 th /17 th century, based their research on intuitions, personal observations, and personal insights. Thus, less time was spent on background reading as there was not much literature to refer to. This is well illustrated in the case of Sir Isaac Newton's apple tree and the theory of gravity, as well as Gregor Mendel's pea plants and the theory of inheritance. However, with the astronomical expansion in scientific knowledge and the emergence of the information age in the last century, new ideas are now being built on previously published works, thus the periodic need to appraise the huge amount of already published literature [ 1 ]. According to Birkle et al. [ 2 ], the Web of Science—an authoritative database of research publications and citations—covered more than 80 million scholarly materials. Hence, a critical review of prior and relevant literature is indispensable for any research endeavour as it provides the necessary framework needed for synthesising new knowledge and for highlighting new insights and perspectives [ 3 ].

Review papers are generally considered secondary research publications that sum up already existing works on a particular research topic or question and relate them to the current status of the topic. This makes review articles distinctly different from scientific research papers. While the primary aim of the latter is to develop new arguments by reporting original research, the former is focused on summarising and synthesising previous ideas, studies, and arguments, without adding new experimental contributions. Review articles basically describe the content and quality of knowledge that are currently available, with a special focus on the significance of the previous works. To this end, a review article cannot simply reiterate a subject matter, but it must contribute to the field of knowledge by synthesising available materials and offering a scholarly critique of theory [ 4 ]. Typically, these articles critically analyse both quantitative and qualitative studies by scrutinising experimental results, the discussion of the experimental data, and in some instances, previous review articles to propose new working theories. Thus, a review article is more than a mere exhaustive compilation of all that has been published on a topic; it must be a balanced, informative, perspective, and unbiased compendium of previous studies which may also include contrasting findings, inconsistencies, and conventional and current views on the subject [ 5 ].

Hence, the essence of a review article is measured by what is achieved, what is discovered, and how information is communicated to the reader [ 6 ]. According to Steward [ 7 ], a good literature review should be analytical, critical, comprehensive, selective, relevant, synthetic, and fully referenced. On the other hand, a review article is considered to be inadequate if it is lacking in focus or outcome, overgeneralised, opinionated, unbalanced, and uncritical [ 7 ]. Most review papers fail to meet these standards and thus can be viewed as mere summaries of previous works in a particular field of study. In one of the few studies that assessed the quality of review articles, none of the 50 papers that were analysed met the predefined criteria for a good review [ 8 ]. However, beginners must also realise that there is no bad writing in the true sense; there is only writing in evolution and under refinement. Literally, every piece of writing can be improved upon, right from the first draft until the final published manuscript. Hence, a paper can only be referred to as bad and unfixable when the author is not open to corrections or when the writer gives up on it.

According to Peat et al. [ 9 ], “everything is easy when you know how,” a maxim which applies to scientific writing in general and review writing in particular. In this regard, the authors emphasized that the writer should be open to learning and should also follow established rules instead of following a blind trial-and-error approach. In contrast to the popular belief that review articles should only be written by experienced scientists and researchers, recent trends have shown that many early-career scientists, especially postgraduate students, are currently expected to write review articles during the course of their studies. However, these scholars have little or no access to formal training on how to analyse and synthesise the research literature in their respective fields [ 10 ]. Consequently, students seeking guidance on how to write or improve their literature reviews are less likely to find published works on the subject, particularly in the science fields. Although various publications have dealt with the challenges of searching for literature, or writing literature reviews for dissertation/thesis purposes, there is little or no information on how to write a comprehensive review article for publication. In addition to the paucity of published information to guide the potential author, the lack of understanding of what constitutes a review paper compounds their challenges. Thus, the purpose of this paper is to serve as a guide for writing review papers for journal publishing. This work draws on the experience of the authors to assist early-career scientists/researchers in the “hard skill” of authoring review articles. Even though there is no single path to writing scientifically, or to writing reviews in particular, this paper attempts to simplify the process by looking at this subject from a beginner's perspective. Hence, this paper highlights the differences between the types of review articles in the sciences while also explaining the needs and purpose of writing review articles. Furthermore, it presents details on how to search for the literature as well as how to structure the manuscript to produce logical and coherent outputs. It is hoped that this work will ease prospective scientific writers into the challenging but rewarding art of writing review articles.

2. Benefits of Review Articles to the Author

Analysing literature gives an overview of the “WHs”: WHat has been reported in a particular field or topic, WHo the key writers are, WHat are the prevailing theories and hypotheses, WHat questions are being asked (and answered), and WHat methods and methodologies are appropriate and useful [ 11 ]. For new or aspiring researchers in a particular field, it can be quite challenging to get a comprehensive overview of their respective fields, especially the historical trends and what has been studied previously. As such, the importance of review articles to knowledge appraisal and contribution cannot be overemphasised, which is reflected in the constant demand for such articles in the research community. However, it is also important for the author, especially the first-time author, to recognise the importance of his/her investing time and effort into writing a quality review article.

Generally, literature reviews are undertaken for many reasons, mainly for publication and for dissertation purposes. The major purpose of literature reviews is to provide direction and information for the improvement of scientific knowledge. They also form a significant component in the research process and in academic assessment [ 12 ]. There may be, however, a thin line between a dissertation literature review and a published review article, given that with some modifications, a literature review can be transformed into a legitimate and publishable scholarly document. According to Gülpınar and Güçlü [ 6 ], the basic motivation for writing a review article is to make a comprehensive synthesis of the most appropriate literature on a specific research inquiry or topic. Thus, conducting a literature review assists in demonstrating the author's knowledge about a particular field of study, which may include but not be limited to its history, theories, key variables, vocabulary, phenomena, and methodologies [ 10 ]. Furthermore, publishing reviews is beneficial as it permits the researchers to examine different questions and, as a result, enhances the depth and diversity of their scientific reasoning [ 1 ]. In addition, writing review articles allows researchers to share insights with the scientific community while identifying knowledge gaps to be addressed in future research. The review writing process can also be a useful tool in training early-career scientists in leadership, coordination, project management, and other important soft skills necessary for success in the research world [ 13 ]. Another important reason for authoring reviews is that such publications have been observed to be remarkably influential, extending the reach of an author in multiple folds of what can be achieved by primary research papers [ 1 ]. The trend in science is for authors to receive more citations from their review articles than from their original research articles. According to Miranda and Garcia-Carpintero [ 14 ], review articles are, on average, three times more frequently cited than original research articles; they also asserted that a 20% increase in review authorship could result in a 40–80% increase in citations of the author. As a result, writing reviews can significantly impact a researcher's citation output and serve as a valuable channel to reach a wider scientific audience. In addition, the references cited in a review article also provide the reader with an opportunity to dig deeper into the topic of interest. Thus, review articles can serve as a valuable repository for consultation, increasing the visibility of the authors and resulting in more citations.

3. Types of Review Articles

The first step in writing a good literature review is to decide on the particular type of review to be written; hence, it is important to distinguish and understand the various types of review articles. Although scientific review articles have been classified according to various schemes, however, they are broadly categorised into narrative reviews, systematic reviews, and meta-analyses [ 15 ]. It was observed that more authors—as well as publishers—were leaning towards systematic reviews and meta-analysis while downplaying narrative reviews; however, the three serve different aims and should all be considered equally important in science [ 1 ]. Bibliometric reviews and patent reviews, which are closely related to meta-analysis, have also gained significant attention recently. However, from another angle, a review could also be of two types. In the first class, authors could deal with a widely studied topic where there is already an accumulated body of knowledge that requires analysis and synthesis [ 3 ]. At the other end of the spectrum, the authors may have to address an emerging issue that would benefit from exposure to potential theoretical foundations; hence, their contribution would arise from the fresh theoretical foundations proposed in developing a conceptual model [ 3 ].

3.1. Narrative Reviews

Narrative reviewers are mainly focused on providing clarification and critical analysis on a particular topic or body of literature through interpretative synthesis, creativity, and expert judgement. According to Green et al. [ 16 ], a narrative review can be in the form of editorials, commentaries, and narrative overviews. However, editorials and commentaries are usually expert opinions; hence, a beginner is more likely to write a narrative overview, which is more general and is also referred to as an unsystematic narrative review. Similarly, the literature review section of most dissertations and empirical papers is typically narrative in nature. Typically, narrative reviews combine results from studies that may have different methodologies to address different questions or to formulate a broad theoretical formulation [ 1 ]. They are largely integrative as strong focus is placed on the assimilation and synthesis of various aspects in the review, which may involve comparing and contrasting research findings or deriving structured implications [ 17 ]. In addition, they are also qualitative studies because they do not follow strict selection processes; hence, choosing publications is relatively more subjective and unsystematic [ 18 ]. However, despite their popularity, there are concerns about their inherent subjectivity. In many instances, when the supporting data for narrative reviews are examined more closely, the evaluations provided by the author(s) become quite questionable [ 19 ]. Nevertheless, if the goal of the author is to formulate a new theory that connects diverse strands of research, a narrative method is most appropriate.

3.2. Systematic Reviews

In contrast to narrative reviews, which are generally descriptive, systematic reviews employ a systematic approach to summarise evidence on research questions. Hence, systematic reviews make use of precise and rigorous criteria to identify, evaluate, and subsequently synthesise all relevant literature on a particular topic [ 12 , 20 ]. As a result, systematic reviews are more likely to inspire research ideas by identifying knowledge gaps or inconsistencies, thus helping the researcher to clearly define the research hypotheses or questions [ 21 ]. Furthermore, systematic reviews may serve as independent research projects in their own right, as they follow a defined methodology to search and combine reliable results to synthesise a new database that can be used for a variety of purposes [ 22 ]. Typically, the peculiarities of the individual reviewer, different search engines, and information databases used all ensure that no two searches will yield the same systematic results even if the searches are conducted simultaneously and under identical criteria [ 11 ]. Hence, attempts are made at standardising the exercise via specific methods that would limit bias and chance effects, prevent duplications, and provide more accurate results upon which conclusions and decisions can be made.

The most established of these methods is the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines which objectively defined statements, guidelines, reporting checklists, and flowcharts for undertaking systematic reviews as well as meta-analysis [ 23 ]. Though mainly designed for research in medical sciences, the PRISMA approach has gained wide acceptance in other fields of science and is based on eight fundamental propositions. These include the explicit definition of the review question, an unambiguous outline of the study protocol, an objective and exhaustive systematic review of reputable literature, and an unambiguous identification of included literature based on defined selection criteria [ 24 ]. Other considerations include an unbiased appraisal of the quality of the selected studies (literature), organic synthesis of the evidence of the study, preparation of the manuscript based on the reporting guidelines, and periodic update of the review as new data emerge [ 24 ]. Other methods such as PRISMA-P (Preferred Reporting Items for Systematic review and Meta-Analysis Protocols), MOOSE (Meta-analysis Of Observational Studies in Epidemiology), and ROSES (Reporting Standards for Systematic Evidence Syntheses) have since been developed for systematic reviews (and meta-analysis), with most of them being derived from PRISMA.

Consequently, systematic reviews—unlike narrative reviews—must contain a methodology section which in addition to all that was highlighted above must fully describe the precise criteria used in formulating the research question and setting the inclusion or exclusion criteria used in selecting/accessing the literature. Similarly, the criteria for evaluating the quality of the literature included in the review as well as for analysing, synthesising, and disseminating the findings must be fully described in the methodology section.

3.3. Meta-Analysis

Meta-analyses are considered as more specialised forms of systematic reviews. Generally, they combine the results of many studies that use similar or closely related methods to address the same question or share a common quantitative evaluation method [ 25 ]. However, meta-analyses are also a step higher than other systematic reviews as they are focused on numerical data and involve the use of statistics in evaluating different studies and synthesising new knowledge. The major advantage of this type of review is the increased statistical power leading to more reliable results for inferring modest associations and a more comprehensive understanding of the true impact of a research study [ 26 ]. Unlike in traditional systematic reviews, research topics covered in meta-analyses must be mature enough to allow the inclusion of sufficient homogeneous empirical research in terms of subjects, interventions, and outcomes [ 27 , 28 ].

Being an advanced form of systematic review, meta-analyses must also have a distinct methodology section; hence, the standard procedures involved in the traditional systematic review (especially PRISMA) also apply in meta-analyses [ 23 ]. In addition to the common steps in formulating systematic reviews, meta-analyses are required to describe how nested and missing data are handled, the effect observed in each study, the confidence interval associated with each synthesised effect, and any potential for bias presented within the sample(s) [ 17 ]. According to Paul and Barari [ 28 ], a meta-analysis must also detail the final sample, the meta-analytic model, and the overall analysis, moderator analysis, and software employed. While the overall analysis involves the statistical characterization of the relationships between variables in the meta-analytic framework and their significance, the moderator analysis defines the different variables that may affect variations in the original studies [ 28 , 29 ]. It must also be noted that the accuracy and reliability of meta-analyses have both been significantly enhanced by the incorporation of statistical approaches such as Bayesian analysis [ 30 ], network analysis [ 31 ], and more recently, machine learning [ 32 ].

3.4. Bibliometric Review

A bibliometric review, commonly referred to as bibliometric analysis, is a systematic evaluation of published works within a specific field or discipline [ 33 ]. This bibliometric methodology involves the use of quantitative methods to analyse bibliometric data such as the characteristics and numbers of publications, units of citations, authorship, co-authorship, and journal impact factors [ 34 ]. Academics use bibliometric analysis with different objectives in mind, which includes uncovering emerging trends in article and journal performance, elaborating collaboration patterns and research constituents, evaluating the impact and influence of particular authors, publications, or research groups, and highlighting the intellectual framework of a certain field [ 35 ]. It is also used to inform policy and decision-making. Similarly to meta-analysis, bibliometric reviews rely upon quantitative techniques, thus avoiding the interpretation bias that could arise from the qualitative techniques of other types of reviews [ 36 ]. However, while bibliometric analysis synthesises the bibliometric and intellectual structure of a field by examining the social and structural linkages between various research parts, meta-analysis focuses on summarising empirical evidence by probing the direction and strength of effects and relationships among variables, especially in open research questions [ 37 , 38 ]. However, similarly to systematic review and meta-analysis, a bibliometric review also requires a well-detailed methodology section. The amount of data to be analysed in bibliometric analysis is quite massive, running to hundreds and tens of thousands in some cases. Although the data are objective in nature (e.g., number of citations and publications and occurrences of keywords and topics), the interpretation is usually carried out through both objective (e.g., performance analysis) and subjective (e.g., thematic analysis) evaluations [ 35 ]. However, the invention and availability of bibliometric software such as BibExcel, Gephi, Leximancer, and VOSviewer and scientific databases such as Dimensions, Web of Science, and Scopus have made this type of analysis more feasible.

3.5. Patent Review

Patent reviews provide a comprehensive analysis and critique of a specific patent or a group of related patents, thus presenting a concise understanding of the technology or innovation that is covered by the patent [ 39 ]. This type of article is useful for researchers as it also enhances their understanding of the legal, technical, and commercial aspects of an intellectual property/innovation; in addition, it is also important for stakeholders outside the research community including IP (intellectual property) specialists, legal professionals, and technology-transfer officers [ 40 ]. Typically, patent reviews encompass the scope, background, claims, legal implications, technical specifications, and potential commercial applications of the patent(s). The article may also include a discussion of the patent's strengths and weaknesses, as well as its potential impact on the industry or field in which it operates. Most times, reviews are time specified, they may be regionalised, and the data are usually retrieved via patent searches on databases such as that of the European Patent Office ( https://www.epo.org/searching.html ), United States Patent and Trademark Office ( https://patft.uspto.gov/ ), the World Intellectual Property Organization's PATENTSCOPE ( https://patentscope.wipo.int/search/en/structuredSearch.jsf ), Google Patent ( https://www.google.com/?tbm=pts ), and China National Intellectual Property Administration ( https://pss-system.cponline.cnipa.gov.cn/conventionalSearch ). According to Cerimi et al. [ 41 ], the retrieved data and analysed may include the patent number, patent status, filing date, application date, grant dates, inventor, assignee, and pending applications. While data analysis is usually carried out by general data software such as Microsoft Excel, an intelligence software solely dedicated to patent research and analysis, Orbit Intelligence has been found to be more efficient [ 39 ]. It is also mandatory to include a methodology section in a patent review, and this should be explicit, thorough, and precise to allow a clear understanding of how the analysis was carried out and how the conclusions were arrived at.

4. Searching Literature

One of the most challenging tasks in writing a review article on a subject is the search for relevant literature to populate the manuscript as the author is required to garner information from an endless number of sources. This is even more challenging as research outputs have been increasing astronomically, especially in the last decade, with thousands of new articles published annually in various fields. It is therefore imperative that the author must not only be aware of the overall trajectory in a field of investigation but must also be cognizant of recent studies so as not to publish outdated research or review articles. Basically, the search for the literature involves a coherent conceptual structuring of the topic itself and a thorough collation of evidence under the common themes which might reflect the histories, conflicts, standoffs, revolutions, and/or evolutions in the field [ 7 ]. To start the search process, the author must carefully identify and select broad keywords relevant to the subject; subsequently, the keywords should be developed to refine the search into specific subheadings that would facilitate the structure of the review.

Two main tactics have been identified for searching the literature, namely, systematic and snowballing [ 42 ]. The systematic approach involves searching literature with specific keywords (for example, cancer, antioxidant, and nanoparticles), which leads to an almost unmanageable and overwhelming list of possible sources [ 43 ]. The snowballing approach, however, involves the identification of a particular publication, followed by the compilation of a bibliography of articles based on the reference list of the identified publication [ 44 ]. Many times, it might be necessary to combine both approaches, but irrespective, the author must keep an accurate track and record of papers cited in the search. A simple and efficient strategy for populating the bibliography of review articles is to go through the abstract (and sometimes the conclusion) of a paper; if the abstract is related to the topic of discourse, the author might go ahead and read the entire article; otherwise, he/she is advised to move on [ 45 ]. Winchester and Salji [ 5 ] noted that to learn the background of the subject/topic to be reviewed, starting literature searches with academic textbooks or published review articles is imperative, especially for beginners. Furthermore, it would also assist in compiling the list of keywords, identifying areas of further exploration, and providing a glimpse of the current state of the research. However, past reviews ideally are not to serve as the foundation of a new review as they are written from someone else's viewpoint, which might have been tainted with some bias. Fortunately, the accessibility and search for the literature have been made relatively easier than they were a few decades ago as the current information age has placed an enormous volume of knowledge right at our fingertips [ 46 ]. Nevertheless, when gathering the literature from the Internet, authors should exercise utmost caution as much of the information may not be verified or peer-reviewed and thus may be unregulated and unreliable. For instance, Wikipedia, despite being a large repository of information with more than 6.7 million articles in the English language alone, is considered unreliable for scientific literature reviews, due to its openness to public editing [ 47 ]. However, in addition to peer-reviewed journal publications—which are most ideal—reviews can also be drawn from a wide range of other sources such as technical documents, in-house reports, conference abstracts, and conference proceedings. Similarly, “Google Scholar”—as against “Google” and other general search engines—is more appropriate as its searches are restricted to only academic articles produced by scholarly societies or/and publishers [ 48 ]. Furthermore, the various electronic databases, such as ScienceDirect, Web of Science, PubMed, and MEDLINE, many of which focus on specific fields of research, are also ideal options [ 49 ]. Advancement in computer indexing has remarkably expanded the ease and ability to search large databases for every potentially relevant article. In addition to searching by topic, literature search can be modified by time; however, there must be a balance between old papers and recent ones. The general consensus in science is that publications less than five years old are considered recent.

It is important, especially in systematic reviews and meta-analyses, that the specific method of running the computer searches be properly documented as there is the need to include this in the method (methodology) section of such papers. Typically, the method details the keywords, databases explored, search terms used, and the inclusion/exclusion criteria applied in the selection of data and any other specific decision/criteria. All of these will ensure the reproducibility and thoroughness of the search and the selection procedure. However, Randolph [ 10 ] noted that Internet searches might not give the exhaustive list of articles needed for a review article; hence, it is advised that authors search through the reference lists of articles that were obtained initially from the Internet search. After determining the relevant articles from the list, the author should read through the references of these articles and repeat the cycle until saturation is reached [ 10 ]. After populating the articles needed for the literature review, the next step is to analyse them individually and in their whole entirety. A systematic approach to this is to identify the key information within the papers, examine them in depth, and synthesise original perspectives by integrating the information and making inferences based on the findings. In this regard, it is imperative to link one source to the other in a logical manner, for instance, taking note of studies with similar methodologies, papers that agree, or results that are contradictory [ 42 ].

5. Structuring the Review Article

The title and abstract are the main selling points of a review article, as most readers will only peruse these two elements and usually go on to read the full paper if they are drawn in by either or both of the two. Tullu [ 50 ] recommends that the title of a scientific paper “should be descriptive, direct, accurate, appropriate, interesting, concise, precise, unique, and not be misleading.” In addition to providing “just enough details” to entice the reader, words in the titles are also used by electronic databases, journal websites, and search engines to index and retrieve a particular paper during a search [ 51 ]. Titles are of different types and must be chosen according to the topic under review. They are generally classified as descriptive, declarative, or interrogative and can also be grouped into compound, nominal, or full-sentence titles [ 50 ]. The subject of these categorisations has been extensively discussed in many articles; however, the reader must also be aware of the compound titles, which usually contain a main title and a subtitle. Typically, subtitles provide additional context—to the main title—and they may specify the geographic scope of the research, research methodology, or sample size [ 52 ].

Just like primary research articles, there are many debates about the optimum length of a review article's title. However, the general consensus is to keep the title as brief as possible while not being too general. A title length between 10 and 15 words is recommended, since longer titles can be more challenging to comprehend. Paiva et al. [ 53 ] observed that articles which contain 95 characters or less get more views and citations. However, emphasis must be placed on conciseness as the audience will be more satisfied if they can understand what exactly the review has contributed to the field, rather than just a hint about the general topic area. Authors should also endeavour to stick to the journal's specific requirements, especially regarding the length of the title and what they should or should not contain [ 9 ]. Thus, avoidance of filler words such as “a review on/of,” “an observation of,” or “a study of” is a very simple way to limit title length. In addition, abbreviations or acronyms should be avoided in the title, except the standard or commonly interpreted ones such as AIDS, DNA, HIV, and RNA. In summary, to write an effective title, the authors should consider the following points. What is the paper about? What was the methodology used? What were the highlights and major conclusions? Subsequently, the author should list all the keywords from these answers, construct a sentence from these keywords, and finally delete all redundant words from the sentence title. It is also possible to gain some ideas by scanning indices and article titles in major journals in the field. It is important to emphasise that a title is not chosen and set in stone, and the title is most likely to be continually revised and adjusted until the end of the writing process.

5.2. Abstract

The abstract, also referred to as the synopsis, is a summary of the full research paper; it is typically independent and can stand alone. For most readers, a publication does not exist beyond the abstract, partly because abstracts are often the only section of a paper that is made available to the readers at no cost, whereas the full paper may attract a payment or subscription [ 54 ]. Thus, the abstract is supposed to set the tone for the few readers who wish to read the rest of the paper. It has also been noted that the abstract gives the first impression of a research work to journal editors, conference scientific committees, or referees, who might outright reject the paper if the abstract is poorly written or inadequate [ 50 ]. Hence, it is imperative that the abstract succinctly represents the entire paper and projects it positively. Just like the title, abstracts have to be balanced, comprehensive, concise, functional, independent, precise, scholarly, and unbiased and not be misleading [ 55 ]. Basically, the abstract should be formulated using keywords from all the sections of the main manuscript. Thus, it is pertinent that the abstract conveys the focus, key message, rationale, and novelty of the paper without any compromise or exaggeration. Furthermore, the abstract must be consistent with the rest of the paper; as basic as this instruction might sound, it is not to be taken for granted. For example, a study by Vrijhoef and Steuten [ 56 ] revealed that 18–68% of 264 abstracts from some scientific journals contained information that was inconsistent with the main body of the publications.

Abstracts can either be structured or unstructured; in addition, they can further be classified as either descriptive or informative. Unstructured abstracts, which are used by many scientific journals, are free flowing with no predefined subheadings, while structured abstracts have specific subheadings/subsections under which the abstract needs to be composed. Structured abstracts have been noted to be more informative and are usually divided into subsections which include the study background/introduction, objectives, methodology design, results, and conclusions [ 57 ]. No matter the style chosen, the author must carefully conform to the instructions provided by the potential journal of submission, which may include but are not limited to the format, font size/style, word limit, and subheadings [ 58 ]. The word limit for abstracts in most scientific journals is typically between 150 and 300 words. It is also a general rule that abstracts do not contain any references whatsoever.

Typically, an abstract should be written in the active voice, and there is no such thing as a perfect abstract as it could always be improved on. It is advised that the author first makes an initial draft which would contain all the essential parts of the paper, which could then be polished subsequently. The draft should begin with a brief background which would lead to the research questions. It might also include a general overview of the methodology used (if applicable) and importantly, the major results/observations/highlights of the review paper. The abstract should end with one or few sentences about any implications, perspectives, or future research that may be developed from the review exercise. Finally, the authors should eliminate redundant words and edit the abstract to the correct word count permitted by the journal [ 59 ]. It is always beneficial to read previous abstracts published in the intended journal, related topics/subjects from other journals, and other reputable sources. Furthermore, the author should endeavour to get feedback on the abstract especially from peers and co-authors. As the abstract is the face of the whole paper, it is best that it is the last section to be finalised, as by this time, the author would have developed a clearer understanding of the findings and conclusions of the entire paper.

5.3. Graphical Abstracts

Since the mid-2000s, an increasing number of journals now require authors to provide a graphical abstract (GA) in addition to the traditional written abstract, to increase the accessibility of scientific publications to readers [ 60 ]. A study showed that publications with GA performed better than those without it, when the abstract views, total citations, and downloads were compared [ 61 ]. However, the GA should provide “a single, concise pictorial, and visual summary of the main findings of an article” [ 62 ]. Although they are meant to be a stand-alone summary of the whole paper, it has been noted that they are not so easily comprehensible without having read through the traditionally written abstract [ 63 ]. It is important to note that, like traditional abstracts, many reputable journals require GAs to adhere to certain specifications such as colour, dimension, quality, file size, and file format (usually JPEG/JPG, PDF, PNG, or TIFF). In addition, it is imperative to use engaging and accurate figures, all of which must be synthesised in order to accurately reflect the key message of the paper. Currently, there are various online or downloadable graphical tools that can be used for creating GAs, such as Microsoft Paint or PowerPoint, Mindthegraph, ChemDraw, CorelDraw, and BioRender.

5.4. Keywords

As a standard practice, journals require authors to select 4–8 keywords (or phrases), which are typically listed below the abstract. A good set of keywords will enable indexers and search engines to find relevant papers more easily and can be considered as a very concise abstract [ 64 ]. According to Dewan and Gupta [ 51 ], the selection of appropriate keywords will significantly enhance the retrieval, accession, and consequently, the citation of the review paper. Ideally, keywords can be variants of the terms/phrases used in the title, the abstract, and the main text, but they should ideally not be the exact words in the main title. Choosing the most appropriate keywords for a review article involves listing down the key terms and phrases in the article, including abbreviations. Subsequently, a quick review of the glossary/vocabulary/term list or indexing standard in the specific discipline will assist in selecting the best and most precise keywords that match those used in the databases from the list drawn. In addition, the keywords should not be broad or general terms (e.g., DNA, biology, and enzymes) but must be specific to the field or subfield of study as well as to the particular paper [ 65 ].

5.5. Introduction

The introduction of an article is the first major section of the manuscript, and it presents basic information to the reader without compelling them to study past publications. In addition, the introduction directs the reader to the main arguments and points developed in the main body of the article while clarifying the current state of knowledge in that particular area of research [ 12 ]. The introduction part of a review article is usually sectionalised into background information, a description of the main topic and finally a statement of the main purpose of the review [ 66 ]. Authors may begin the introduction with brief general statements—which provide background knowledge on the subject matter—that lead to more specific ones [ 67 ]. It is at this point that the reader's attention must be caught as the background knowledge must highlight the importance and justification for the subject being discussed, while also identifying the major problem to be addressed [ 68 ]. In addition, the background should be broad enough to attract even nonspecialists in the field to maximise the impact and widen the reach of the article. All of these should be done in the light of current literature; however, old references may also be used for historical purposes. A very important aspect of the introduction is clearly stating and establishing the research problem(s) and how a review of the particular topic contributes to those problem(s). Thus, the research gap which the paper intends to fill, the limitations of previous works and past reviews, if available, and the new knowledge to be contributed must all be highlighted. Inadequate information and the inability to clarify the problem will keep readers (who have the desire to obtain new information) from reading beyond the introduction [ 69 ]. It is also pertinent that the author establishes the purpose of reviewing the literature and defines the scope as well as the major synthesised point of view. Furthermore, a brief insight into the criteria used to select, evaluate, and analyse the literature, as well as the outline or sequence of the review, should be provided in the introduction. Subsequently, the specific objectives of the review article must be presented. The last part of the “introduction” section should focus on the solution, the way forward, the recommendations, and the further areas of research as deduced from the whole review process. According to DeMaria [ 70 ], clearly expressed or recommended solutions to an explicitly revealed problem are very important for the wholesomeness of the “introduction” section. It is believed that following these steps will give readers the opportunity to track the problems and the corresponding solution from their own perspective in the light of current literature. As against some suggestions that the introduction should be written only in present tenses, it is also believed that it could be done with other tenses in addition to the present tense. In this regard, general facts should be written in the present tense, specific research/work should be in the past tense, while the concluding statement should be in the past perfect or simple past. Furthermore, many of the abbreviations to be used in the rest of the manuscript and their explanations should be defined in this section.

5.6. Methodology

Writing a review article is equivalent to conducting a research study, with the information gathered by the author (reviewer) representing the data. Like all major studies, it involves conceptualisation, planning, implementation, and dissemination [ 71 ], all of which may be detailed in a methodology section, if necessary. Hence, the methodological section of a review paper (which can also be referred to as the review protocol) details how the relevant literature was selected and how it was analysed as well as summarised. The selection details may include, but are not limited to, the database consulted and the specific search terms used together with the inclusion/exclusion criteria. As earlier highlighted in Section 3 , a description of the methodology is required for all types of reviews except for narrative reviews. This is partly because unlike narrative reviews, all other review articles follow systematic approaches which must ensure significant reproducibility [ 72 ]. Therefore, where necessary, the methods of data extraction from the literature and data synthesis must also be highlighted as well. In some cases, it is important to show how data were combined by highlighting the statistical methods used, measures of effect, and tests performed, as well as demonstrating heterogeneity and publication bias [ 73 ].

The methodology should also detail the major databases consulted during the literature search, e.g., Dimensions, ScienceDirect, Web of Science, MEDLINE, and PubMed. For meta-analysis, it is imperative to highlight the software and/or package used, which could include Comprehensive Meta-Analysis, OpenMEE, Review Manager (RevMan), Stata, SAS, and R Studio. It is also necessary to state the mathematical methods used for the analysis; examples of these include the Bayesian analysis, the Mantel–Haenszel method, and the inverse variance method. The methodology should also state the number of authors that carried out the initial review stage of the study, as it has been recommended that at least two reviews should be done blindly and in parallel, especially when it comes to the acquisition and synthesis of data [ 74 ]. Finally, the quality and validity assessment of the publication used in the review must be stated and well clarified [ 73 ].

5.7. Main Body of the Review

Ideally, the main body of a publishable review should answer these questions: What is new (contribution)? Why so (logic)? So what (impact)? How well it is done (thoroughness)? The flow of the main body of a review article must be well organised to adequately maintain the attention of the readers as well as guide them through the section. It is recommended that the author should consider drawing a conceptual scheme of the main body first, using methods such as mind-mapping. This will help create a logical flow of thought and presentation, while also linking the various sections of the manuscript together. According to Moreira [ 75 ], “reports do not simply yield their findings, rather reviewers make them yield,” and thus, it is the author's responsibility to transform “resistant” texts into “docile” texts. Hence, after the search for the literature, the essential themes and key concepts of the review paper must be identified and synthesised together. This synthesis primarily involves creating hypotheses about the relationships between the concepts with the aim of increasing the understanding of the topic being reviewed. The important information from the various sources should not only be summarised, but the significance of studies must be related back to the initial question(s) posed by the review article. Furthermore, MacLure [ 76 ] stated that data are not just to be plainly “extracted intact” and “used exactly as extracted,” but must be modified, reconfigured, transformed, transposed, converted, tabulated, graphed, or manipulated to enable synthesis, combination, and comparison. Therefore, different pieces of information must be extracted from the reports in which they were previously deposited and then refined into the body of the new article [ 75 ]. To this end, adequate comparison and combination might require that “qualitative data be quantified” or/and “quantitative data may be qualitized” [ 77 ]. In order to accomplish all of these goals, the author may have to transform, paraphrase, generalize, specify, and reorder the text [ 78 ]. For comprehensiveness, the body paragraphs should be arranged in a similar order as it was initially stated in the abstract or/and introduction. Thus, the main body could be divided into thematic areas, each of which could be independently comprehensive and treated as a mini review. Similarly, the sections can also be arranged chronologically depending on the focus of the review. Furthermore, the abstractions should proceed from a wider general view of the literature being reviewed and then be narrowed down to the specifics. In the process, deep insights should also be provided between the topic of the review and the wider subject area, e.g., fungal enzymes and enzymes in general. The abstractions must also be discussed in more detail by presenting more specific information from the identified sources (with proper citations of course!). For example, it is important to identify and highlight contrary findings and rival interpretations as well as to point out areas of agreement or debate among different bodies of literature. Often, there are previous reviews on the same topic/concept; however, this does not prevent a new author from writing one on the same topic, especially if the previous reviews were written many years ago. However, it is important that the body of the new manuscript be written from a new angle that was not adequately covered in the past reviews and should also incorporate new studies that have accumulated since the last review(s). In addition, the new review might also highlight the approaches, limitations, and conclusions of the past studies. But the authors must not be excessively critical of the past reviews as this is regarded by many authors as a sign of poor professionalism [ 3 , 79 ]. Daft [ 79 ] emphasized that it is more important for a reviewer to state how their research builds on previous work instead of outright claiming that previous works are incompetent and inadequate. However, if a series of related papers on one topic have a common error or research flaw that needs rectification, the reviewer must point this out with the aim of moving the field forward [ 3 ]. Like every other scientific paper, the main body of a review article also needs to be consistent in style, for example, in the choice of passive vs. active voice and present vs. past tense. It is also important to note that tables and figures can serve as a powerful tool for highlighting key points in the body of the review, and they are now considered core elements of reviews. For more guidance and insights into what should make up the contents of a good review article, readers are also advised to get familiarised with the Boote and Beile [ 80 ] literature review scoring rubric as well as the review article checklist of Short [ 81 ].

5.8. Tables and Figures

An ideal review article should be logically structured and efficiently utilise illustrations, in the form of tables and figures, to convey the key findings and relationships in the study. According to Tay [ 13 ], illustrations often take a secondary role in review papers when compared to primary research papers which are focused on illustrations. However, illustrations are very important in review articles as they can serve as succinct means of communicating major findings and insights. Franzblau and Chung [ 82 ] pointed out that illustrations serve three major purposes in a scientific article: they simplify complex data and relationships for better understanding, they minimise reading time by summarising and bringing to focus on the key findings (or trends), and last, they help to reduce the overall word count. Hence, inserting and constructing illustrations in a review article is as meticulous as it is important. However, important decisions should be made on whether the charts, figures, or tables to be potentially inserted in the manuscript are indeed needed and how best to design them [ 83 ]. Illustrations should enhance the text while providing necessary information; thus, the information described in illustrations should not contradict that in the main text and should also not be a repetition of texts [ 84 ]. Furthermore, illustrations must be autonomous, meaning they ought to be intelligible without having to read the text portion of the manuscript; thus, the reader does not have to flip back and forth between the illustration and the main text in order to understand it [ 85 ]. It should be noted that tables or figures that directly reiterate the main text or contain extraneous information will only make a mess of the manuscript and discourage readers [ 86 ].

Kotz and Cals [ 87 ] recommend that the layout of tables and figures should be carefully designed in a clear manner with suitable layouts, which will allow them to be referred to logically and chronologically in the text. In addition, illustrations should only contain simple text, as lengthy details would contradict their initial objective, which was to provide simple examples or an overview. Furthermore, the use of abbreviations in illustrations, especially tables, should be avoided if possible. If not, the abbreviations should be defined explicitly in the footnotes or legends of the illustration [ 88 ]. Similarly, numerical values in tables and graphs should also be correctly approximated [ 84 ]. It is recommended that the number of tables and figures in the manuscript should not exceed the target journal's specification. According to Saver [ 89 ], they ideally should not account for more than one-third of the manuscript. Finally, the author(s) must seek permission and give credits for using an already published illustration when necessary. However, none of these are needed if the graphic is originally created by the author, but if it is a reproduced or an adapted illustration, the author must obtain permission from the copyright owner and include the necessary credit. One of the very important tools for designing illustrations is Creative Commons, a platform that provides a wide range of creative works which are available to the public for use and modification.

5.9. Conclusion/Future Perspectives

It has been observed that many reviews end abruptly with a short conclusion; however, a lot more can be included in this section in addition to what has been said in the major sections of the paper. Basically, the conclusion section of a review article should provide a summary of key findings from the main body of the manuscript. In this section, the author needs to revisit the critical points of the paper as well as highlight the accuracy, validity, and relevance of the inferences drawn in the article review. A good conclusion should highlight the relationship between the major points and the author's hypothesis as well as the relationship between the hypothesis and the broader discussion to demonstrate the significance of the review article in a larger context. In addition to giving a concise summary of the important findings that describe current knowledge, the conclusion must also offer a rationale for conducting future research [ 12 ]. Knowledge gaps should be identified, and themes should be logically developed in order to construct conceptual frameworks as well as present a way forward for future research in the field of study [ 11 ].

Furthermore, the author may have to justify the propositions made earlier in the manuscript, demonstrate how the paper extends past research works, and also suggest ways that the expounded theories can be empirically examined [ 3 ]. Unlike experimental studies which can only draw either a positive conclusion or ambiguous failure to reject the null hypothesis, four possible conclusions can be drawn from review articles [ 1 ]. First, the theory/hypothesis propounded may be correct after being proven from current evidence; second, the hypothesis may not be explicitly proven but is most probably the best guess. The third conclusion is that the currently available evidence does not permit a confident conclusion or a best guess, while the last conclusion is that the theory or hypothesis is false [ 1 ]. It is important not to present new information in the conclusion section which has link whatsoever with the rest of the manuscript. According to Harris et al. [ 90 ], the conclusions should, in essence, answer the question: if a reader were to remember one thing about the review, what would it be?

5.10. References

As it has been noted in different parts of this paper, authors must give the required credit to any work or source(s) of information that was included in the review article. This must include the in-text citations in the main body of the paper and the corresponding entries in the reference list. Ideally, this full bibliographical list is the last part of the review article, and it should contain all the books, book chapters, journal articles, reports, and other media, which were utilised in the manuscript. It has been noted that most journals and publishers have their own specific referencing styles which are all derived from the more popular styles such as the American Psychological Association (APA), Chicago, Harvard, Modern Language Association (MLA), and Vancouver styles. However, all these styles may be categorised into either the parenthetical or numerical referencing style. Although a few journals do not have strict referencing rules, it is the responsibility of the author to reference according to the style and instructions of the journal. Omissions and errors must be avoided at all costs, and this can be easily achieved by going over the references many times for due diligence [ 11 ]. According to Cronin et al. [ 12 ], a separate file for references can be created, and any work used in the manuscript can be added to this list immediately after being cited in the text [ 12 ]. In recent times, the emergence of various referencing management software applications such as Endnote, RefWorks, Mendeley, and Zotero has even made referencing easier. The majority of these software applications require little technical expertise, and many of them are free to use, while others may require a subscription. It is imperative, however, that even after using these software packages, the author must manually curate the references during the final draft, in order to avoid any errors, since these programs are not impervious to errors, particularly formatting errors.

6. Concluding Remarks

Writing a review article is a skill that needs to be learned; it is a rigorous but rewarding endeavour as it can provide a useful platform to project the emerging researcher or postgraduate student into the gratifying world of publishing. Thus, the reviewer must develop the ability to think critically, spot patterns in a large volume of information, and must be invested in writing without tiring. The prospective author must also be inspired and dedicated to the successful completion of the article while also ensuring that the review article is not just a mere list or summary of previous research. It is also important that the review process must be focused on the literature and not on the authors; thus, overt criticism of existing research and personal aspersions must be avoided at all costs. All ideas, sentences, words, and illustrations should be constructed in a way to avoid plagiarism; basically, this can be achieved by paraphrasing, summarising, and giving the necessary acknowledgments. Currently, there are many tools to track and detect plagiarism in manuscripts, ensuring that they fall within a reasonable similarity index (which is typically 15% or lower for most journals). Although the more popular of these tools, such as Turnitin and iThenticate, are subscription-based, there are many freely available web-based options as well. An ideal review article is supposed to motivate the research topic and describe its key concepts while delineating the boundaries of research. In this regard, experience-based information on how to methodologically develop acceptable and impactful review articles has been detailed in this paper. Furthermore, for a beginner, this guide has detailed “the why” and “the how” of authoring a good scientific review article. However, the information in this paper may as a whole or in parts be also applicable to other fields of research and to other writing endeavours such as writing literature review in theses, dissertations, and primary research articles. Finally, the intending authors must put all the basic rules of scientific writing and writing in general into cognizance. A comprehensive study of the articles cited within this paper and other related articles focused on scientific writing will further enhance the ability of the motivated beginner to deliver a good review article.

Acknowledgments

This work was supported by the National Research Foundation of South Africa under grant number UID 138097. The authors would like to thank the Durban University of Technology for funding the postdoctoral fellowship of the first author, Dr. Ayodeji Amobonye.

Data Availability

Conflicts of interest.

The authors declare that they have no conflicts of interest.

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8.1: What’s a Critique and Why Does it Matter?

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Steven D. Krause
Eastern Michigan University

Critiques evaluate and analyze a wide variety of things (texts, images, performances, etc.) based on reasons or criteria. Sometimes, people equate the notion of “critique” to “criticism,” which usually suggests a negative interpretation. These terms are easy to confuse, but I want to be clear that critique and criticize don’t mean the same thing. A negative critique might be said to be “criticism” in the way we often understand the term “to criticize,” but critiques can be positive too.

We’re all familiar with one of the most basic forms of critique: reviews (film reviews, music reviews, art reviews, book reviews, etc.). Critiques in the form of reviews tend to have a fairly simple and particular point: whether or not something is “good” or “bad.”

Academic critiques are similar to the reviews we see in popular sources in that critique writers are trying to make a particular point about whatever it is that they are critiquing. But there are some differences between the sorts of critiques we read in academic sources versus the ones we read in popular sources.

The subjects of academic critiques tend to be other academic writings and they frequently appear in scholarly journals.
Academic critiques frequently go further in making an argument beyond a simple assessment of the quality of a particular book, film, performance, or work of art. Academic critique writers will often compare and discuss several works that are similar to each other to make some larger point. In other words, instead of simply commenting on whether something was good or bad, academic critiques tend to explore issues and ideas in ways that are more complicated than merely “good” or “bad.”

The main focus of this chapter is the value of writing critiques as a part of the research writing process. Critiquing writing is important because in order to write a good critique you need to critically read : that is, you need to closely read and understand whatever it is you are critiquing, you need to apply appropriate criteria in order evaluate it, you need to summarize it, and to ultimately make some sort of point about the text you are critiquing.

These skills-- critically and closely reading, summarizing, creating and applying criteria, and then making an evaluation-- are key to The Process of Research Writing, and they should help you as you work through the process of research writing.

In this chapter, I’ve provided a “step-by-step” process for making a critique. I would encourage you to quickly read or skim through this chapter first, and then go back and work through the steps and exercises describe.

Selecting the right text to critique

The first step in writing a critique is selecting a text to critique. For the purposes of this writing exercise, you should check with your teacher for guidelines on what text to pick. If you are doing an annotated bibliography as part of your research project (see chapter 6, “The Annotated Bibliography Exercise”), then you are might find more materials that will work well for this project as you continuously research.

Short and simple newspaper articles, while useful as part of the research process, can be difficult to critique since they don’t have the sort of detail that easily allows for a critical reading. On the other hand, critiquing an entire book is probably a more ambitious task than you are likely to have time or energy for with this exercise. Instead, consider critiquing one of the more fully developed texts you’ve come across in your research: an in-depth examination from a news magazine, a chapter from a scholarly book, a report on a research study or experiment, or an analysis published in an academic journal. These more complex essays usually present more opportunities for issues to critique.

Depending on your teacher’s assignment, the “text” you critique might include something that isn’t in writing: a movie, a music CD, a multimedia presentation, a computer game, a painting, etc. As is the case with more traditional writings, you want to select a text that has enough substance to it so that it stands up to a critical reading.

Exercise 7.1

Pick out at least three different possibilities for texts that you could critique for this exercise. If you’ve already started work on your research and an annotated bibliography for your research topic, you should consider those pieces of research as possibilities. Working alone or in small groups, consider the potential of each text. Here are some questions to think about:

Does the text provide in-depth information? How long is it? Does it include a “works cited” or bibliography section?
What is the source of the text? Does it come from an academic, professional, or scholarly publication?
Does the text advocate a particular position? What is it, and do you agree or disagree with the text?

Physical Review Research

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Accepted Paper

Maximizing quantum-computing expressive power through randomized circuits

Phys. rev. research, yingli yang, zongkang zhang, anbang wang, xiaosi xu, xiaoting wang, and ying li.

In the noisy intermediate-scale quantum era, variational quantum algorithms (VQAs) have emerged as a promising avenue to obtain quantum advantage. However, the success of VQAs depends on the expressive power of parameterised quantum circuits, which is constrained by the limited gate number and the presence of barren plateaus. In this work, we propose and numerically demonstrate a novel approach for VQAs, utilizing randomised quantum circuits to generate the variational wavefunction. We parameterize the distribution function of these random circuits using artificial neural networks and optimize it to find the solution. This random-circuit approach presents a trade-off between maximizing the expressive power of the variational wavefunction and minimizing the associated time cost, specifically the sampling cost of quantum circuits. Given a fixed gate number, we can systematically increase the expressive power by extending the quantum-computing time. With a sufficiently large permissible time cost, the variational wavefunction can approximate any quantum state with arbitrary accuracy. Furthermore, we establish explicit relationships between expressive power, time cost, and gate number for variational quantum eigensolvers. These results highlight the promising potential of the random-circuit approach in achieving a high expressive power in quantum computing.

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Solar Eclipse Eye Protection: How to Verify the Legitimacy of Your Glasses

T his year's total solar eclipse is just 2 days away -- April 8, 2024 . The eclipse will be the last one in the continental United States for almost 20 years. If you live in the path of totality or are traveling to it, you're going to need solar eclipse glasses to protect your eyes while staring at the sun.

Solar eclipse glasses are special glasses that block out the most dangerous parts of the solar spectrum for human eyes. When you look through them, the sun should appear as an easy-to-view yellow-orange circle. But you should be aware that these glasses will block out all light -- so you won't want to use these glasses while walking, driving or doing anything besides eclipse viewing.

Read more: Need Free Solar Eclipse Glasses? Stop by Warby Parker

However, bad actors will sell eclipse glasses that don't actually do anything to protect your eyes from the sun. So, if you're viewing the eclipse in person this year, you're going to want to make sure that your eyes are really being protected. Read on to find out about the steps you can take to make sure your solar eclipse glasses are legit.

For more, here's how Solar Snap can help you take great eclipse photos and why you should download your Google Maps route before you travel to see the eclipse.

Check the ISO number of your eclipse glasses

According to the American Astronomical Society , a real and safe pair of solar eclipse glasses should be labeled with ISO 12312-2 (sometimes written in more detail as ISO 12312-2:2015), which is an international safety standard that denotes the glasses reduce visible sunlight to safe levels and block UV and IR radiation.

Check the list of reputable eclipse glasses vendors

However, fake glasses may also be labeled as being compliant with ISO 12312-2 because, as a general rule, people are greedy, selfish and not to be trusted. To double-check the veracity of your eclipse glasses' ISO claims, you can see if the vendor from which you purchased the shades is trustworthy in the eyes of the AAS. See its list of Reputable Vendors of Solar Filters and Viewers . Really, the safest thing you can do is pick a vendor from the above list and purchase your glasses from there, so there are no concerns about counterfeits and fakes when it comes to your eye safety.

The list also includes big-box retailers and chains where you can grab AAS approved eclipse glasses, including Warby Parker , which is giving glasses away for free starting April 1. Personally, I got my glasses from a trusted local museum, but I'll still be checking mine to make sure that I'll be protected.

In assembling its list, the AAS checks to make sure a manufacturer earned its ISO rating with proper, labs-based testing. It also asks manufacturers for their authorized resellers and resellers for their manufacturers. If the vendor of your eclipse shades is listed, then you are safe. But the opposite isn't necessarily true. If your vendor isn't listed, it doesn't necessarily mean they are slinging counterfeits. It just means the AAS hasn't checked them out or hasn't been able to track everything down.

So, what are you to do if your vendor isn't on the list? Perform an eye test.

How to test your solar eclipse glasses

If your mystery pair of eclipse glasses look pretty darn dark, that's a good place to start start. You should not be able to see anything through them except the sun itself or something similarly bright.

What's something as bright as the sun you can use as a test? The AAS suggests you check sunlight reflected off a mirror or a shiny metal object. If sun is behind the clouds or on the other side of the earth when you want to test your glasses, you can use a bright-white LED such as the flashlight on your phone or a bare lightbulb. The reflected sunlight or bright, white, artificial light should appear very dim through a safe pair of eclipse glasses. If you can see light behind a lamp shade or a soft, frosted light bulb through the glasses through your eclipse glasses, then you know that these glasses aren't strong enough to stare safely at the sun.

When staring at the sun through safe solar eclipse glasses, the sun should appear comfortably bright like the full moon, according to the AAS. If your eclipse glasses are uncomfortable to use, that is also a good sign that they might not be legitimate.

Total eclipse image taken Mar. 20, 2015 at Svalbard, Norway.

Open access
Published: 12 December 2023

Examining the role of community resilience and social capital on mental health in public health emergency and disaster response: a scoping review

C. E. Hall 1 , 2 ,
H. Wehling 1 ,
J. Stansfield 3 ,
J. South 3 ,
S. K. Brooks 2 ,
N. Greenberg 2 , 4 ,
R. Amlôt 1 &
D. Weston 1

BMC Public Health volume 23 , Article number: 2482 ( 2023 ) Cite this article

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The ability of the public to remain psychologically resilient in the face of public health emergencies and disasters (such as the COVID-19 pandemic) is a key factor in the effectiveness of a national response to such events. Community resilience and social capital are often perceived as beneficial and ensuring that a community is socially and psychologically resilient may aid emergency response and recovery. This review presents a synthesis of literature which answers the following research questions: How are community resilience and social capital quantified in research?; What is the impact of community resilience on mental wellbeing?; What is the impact of infectious disease outbreaks, disasters and emergencies on community resilience and social capital?; and, What types of interventions enhance community resilience and social capital?

A scoping review procedure was followed. Searches were run across Medline, PsycInfo, and EMBASE, with search terms covering both community resilience and social capital, public health emergencies, and mental health. 26 papers met the inclusion criteria.

The majority of retained papers originated in the USA, used a survey methodology to collect data, and involved a natural disaster. There was no common method for measuring community resilience or social capital. The association between community resilience and social capital with mental health was regarded as positive in most cases. However, we found that community resilience, and social capital, were initially negatively impacted by public health emergencies and enhanced by social group activities.

Several key recommendations are proposed based on the outcomes from the review, which include: the need for a standardised and validated approach to measuring both community resilience and social capital; that there should be enhanced effort to improve preparedness to public health emergencies in communities by gauging current levels of community resilience and social capital; that community resilience and social capital should be bolstered if areas are at risk of disasters or public health emergencies; the need to ensure that suitable short-term support is provided to communities with high resilience in the immediate aftermath of a public health emergency or disaster; the importance of conducting robust evaluation of community resilience initiatives deployed during the COVID-19 pandemic.

Peer Review reports

For the general population, public health emergencies and disasters (e.g., natural disasters; infectious disease outbreaks; Chemical, Biological, Radiological or Nuclear incidents) can give rise to a plethora of negative outcomes relating to both health (e.g. increased mental health problems [ 1 , 2 , 3 , 4 ]) and the economy (e.g., increased unemployment and decreased levels of tourism [ 4 , 5 , 6 ]). COVID-19 is a current, and ongoing, example of a public health emergency which has affected over 421 million individuals worldwide [ 7 ]. The long term implications of COVID-19 are not yet known, but there are likely to be repercussions for physical health, mental health, and other non-health related outcomes for a substantial time to come [ 8 , 9 ]. As a result, it is critical to establish methods which may inform approaches to alleviate the longer-term negative consequences that are likely to emerge in the aftermath of both COVID-19 and any future public health emergency.

The definition of resilience often differs within the literature, but ultimately resilience is considered a dynamic process of adaptation. It is related to processes and capabilities at the individual, community and system level that result in good health and social outcomes, in spite of negative events, serious threats and hazards [ 10 ]. Furthermore, Ziglio [ 10 ] refers to four key types of resilience capacity: adaptive, the ability to withstand and adjust to unfavourable conditions and shocks; absorptive, the ability to withstand but also to recover and manage using available assets and skills; anticipatory, the ability to predict and minimize vulnerability; and transformative, transformative change so that systems better cope with new conditions.

There is no one settled definition of community resilience (CR). However, it generally relates to the ability of a community to withstand, adapt and permit growth in adverse circumstances due to social structures, networks and interdependencies within the community [ 11 ]. Social capital (SC) is considered a major determinant of CR [ 12 , 13 ], and reflects strength of a social network, community reciprocity, and trust in people and institutions [ 14 ]. These aspects of community are usually conceptualised primarily as protective factors that enable communities to cope and adapt collectively to threats. SC is often broken down into further categories [ 15 ], for example: cognitive SC (i.e. perceptions of community relations, such as trust, mutual help and attachment) and structural SC (i.e. what actually happens within the community, such as participation, socialising) [ 16 ]; or, bonding SC (i.e. connections among individuals who are emotionally close, and result in bonds to a particular group [ 17 ]) and bridging SC (i.e. acquaintances or individuals loosely connected that span different social groups [ 18 ]). Generally, CR is perceived to be primarily beneficial for multiple reasons (e.g. increased social support [ 18 , 19 ], protection of mental health [ 20 , 21 ]), and strengthening community resilience is a stated health goal of the World Health Organisation [ 22 ] when aiming to alleviate health inequalities and protect wellbeing. This is also reflected by organisations such as Public Health England (now split into the UK Health Security Agency and the Office for Health Improvement and Disparities) [ 23 ] and more recently, CR has been targeted through the endorsement of Community Champions (who are volunteers trained to support and to help improve health and wellbeing. Community Champions also reflect their local communities in terms of population demographics for example age, ethnicity and gender) as part of the COVID-19 response in the UK (e.g. [ 24 , 25 ]).

Despite the vested interest in bolstering communities, the research base establishing: how to understand and measure CR and SC; the effect of CR and SC, both during and following a public health emergency (such as the COVID-19 pandemic); and which types of CR or SC are the most effective to engage, is relatively small. Given the importance of ensuring resilience against, and swift recovery from, public health emergencies, it is critically important to establish and understand the evidence base for these approaches. As a result, the current review sought to answer the following research questions: (1) How are CR and SC quantified in research?; (2) What is the impact of community resilience on mental wellbeing?; (3) What is the impact of infectious disease outbreaks, disasters and emergencies on community resilience and social capital?; and, (4) What types of interventions enhance community resilience and social capital?

By collating research in order to answer these research questions, the authors have been able to propose several key recommendations that could be used to both enhance and evaluate CR and SC effectively to facilitate the long-term recovery from COVID-19, and also to inform the use of CR and SC in any future public health disasters and emergencies.

A scoping review methodology was followed due to the ease of summarising literature on a given topic for policy makers and practitioners [ 26 ], and is detailed in the following sections.

Identification of relevant studies

An initial search strategy was developed by authors CH and DW and included terms which related to: CR and SC, given the absence of a consistent definition of CR, and the link between CR and SC, the review focuses on both CR and SC to identify as much relevant literature as possible (adapted for purpose from Annex 1: [ 27 ], as well as through consultation with review commissioners); public health emergencies and disasters [ 28 , 29 , 30 , 31 ], and psychological wellbeing and recovery (derived a priori from literature). To ensure a focus on both public health and psychological research, the final search was carried across Medline, PsycInfo, and EMBASE using OVID. The final search took place on the 18th of May 2020, the search strategy used for all three databases can be found in Supplementary file 1 .

Selection criteria

The inclusion and exclusion criteria were developed alongside the search strategy. Initially the criteria were relatively inclusive and were subject to iterative development to reflect the authors’ familiarisation with the literature. For example, the decision was taken to exclude research which focused exclusively on social support and did not mention communities as an initial title/abstract search suggested that the majority of this literature did not meet the requirements of our research question.

The full and final inclusion and exclusion criteria used can be found in Supplementary file 2 . In summary, authors decided to focus on the general population (i.e., non-specialist, e.g. non-healthcare worker or government official) to allow the review to remain community focused. The research must also have assessed the impact of CR and/or SC on mental health and wellbeing, resilience, and recovery during and following public health emergencies and infectious disease outbreaks which affect communities (to ensure the research is relevant to the review aims), have conducted primary research, and have a full text available or provided by the first author when contacted.

Charting the data

All papers were first title and abstract screened by CH or DW. Papers then were full text reviewed by CH to ensure each paper met the required eligibility criteria, if unsure about a paper it was also full text reviewed by DW. All papers that were retained post full-text review were subjected to a standardised data extraction procedure. A table was made for the purpose of extracting the following data: title, authors, origin, year of publication, study design, aim, disaster type, sample size and characteristics, variables examined, results, restrictions/limitations, and recommendations. Supplementary file 3 details the charting the data process.

Analytical method

Data was synthesised using a Framework approach [ 32 ], a common method for analysing qualitative research. This method was chosen as it was originally used for large-scale social policy research [ 33 ] as it seeks to identify: what works, for whom, in what conditions, and why [ 34 ]. This approach is also useful for identifying commonalities and differences in qualitative data and potential relationships between different parts of the data [ 33 ]. An a priori framework was established by CH and DW. Extracted data was synthesised in relation to each research question, and the process was iterative to ensure maximum saturation using the available data.

Study selection

The final search strategy yielded 3584 records. Following the removal of duplicates, 2191 records remained and were included in title and abstract screening. A PRISMA flow diagram is presented in Fig. 1 .

PRISMA flow diagram

At the title and abstract screening stage, the process became more iterative as the inclusion criteria were developed and refined. For the first iteration of screening, CH or DW sorted all records into ‘include,’ ‘exclude,’ and ‘unsure’. All ‘unsure’ papers were re-assessed by CH, and a random selection of ~ 20% of these were also assessed by DW. Where there was disagreement between authors the records were retained, and full text screened. The remaining papers were reviewed by CH, and all records were categorised into ‘include’ and ‘exclude’. Following full-text screening, 26 papers were retained for use in the review.

Study characteristics

This section of the review addresses study characteristics of those which met the inclusion criteria, which comprises: date of publication, country of origin, study design, study location, disaster, and variables examined.

Date of publication

Publication dates across the 26 papers spanned from 2008 to 2020 (see Fig. 2 ). The number of papers published was relatively low and consistent across this timescale (i.e. 1–2 per year, except 2010 and 2013 when none were published) up until 2017 where the number of papers peaked at 5. From 2017 to 2020 there were 15 papers published in total. The amount of papers published in recent years suggests a shift in research and interest towards CR and SC in a disaster/ public health emergency context.

Graph to show retained papers date of publication

Country of origin

The locations of the first authors’ institutes at the time of publication were extracted to provide a geographical spread of the retained papers. The majority originated from the USA [ 35 , 36 , 37 , 38 , 39 , 40 , 41 ], followed by China [ 42 , 43 , 44 , 45 , 46 ], Japan [ 47 , 48 , 49 , 50 ], Australia [ 51 , 52 , 53 ], The Netherlands [ 54 , 55 ], New Zealand [ 56 ], Peru [ 57 ], Iran [ 58 ], Austria [ 59 ], and Croatia [ 60 ].

There were multiple methodological approaches carried out across retained papers. The most common formats included surveys or questionnaires [ 36 , 37 , 38 , 42 , 46 , 47 , 48 , 49 , 50 , 53 , 54 , 55 , 57 , 59 ], followed by interviews [ 39 , 40 , 43 , 51 , 52 , 60 ]. Four papers used both surveys and interviews [ 35 , 41 , 45 , 58 ], and two papers conducted data analysis (one using open access data from a Social Survey [ 44 ] and one using a Primary Health Organisations Register [ 56 ]).

Study location

The majority of the studies were carried out in Japan [ 36 , 42 , 44 , 47 , 48 , 49 , 50 ], followed by the USA [ 35 , 37 , 38 , 39 , 40 , 41 ], China [ 43 , 45 , 46 , 53 ], Australia [ 51 , 52 ], and the UK [ 54 , 55 ]. The remaining studies were carried out in Croatia [ 60 ], Peru [ 57 ], Austria [ 59 ], New Zealand [ 56 ] and Iran [ 58 ].

Multiple different types of disaster were researched across the retained papers. Earthquakes were the most common type of disaster examined [ 45 , 47 , 49 , 50 , 53 , 56 , 57 , 58 ], followed by research which assessed the impact of two disastrous events which had happened in the same area (e.g. Hurricane Katrina and the Deepwater Horizon oil spill in Mississippi, and the Great East Japan earthquake and Tsunami; [ 36 , 37 , 38 , 42 , 44 , 48 ]). Other disaster types included: flooding [ 51 , 54 , 55 , 59 , 60 ], hurricanes [ 35 , 39 , 41 ], infectious disease outbreaks [ 43 , 46 ], oil spillage [ 40 ], and drought [ 52 ].

Variables of interest examined

Across the 26 retained papers: eight referred to examining the impact of SC [ 35 , 37 , 39 , 41 , 46 , 49 , 55 , 60 ]; eight examined the impact of cognitive and structural SC as separate entities [ 40 , 42 , 45 , 48 , 50 , 54 , 57 , 59 ]; one examined bridging and bonding SC as separate entities [ 58 ]; two examined the impact of CR [ 38 , 56 ]; and two employed a qualitative methodology but drew findings in relation to bonding and bridging SC, and SC generally [ 51 , 52 ]. Additionally, five papers examined the impact of the following variables: ‘community social cohesion’ [ 36 ], ‘neighbourhood connectedness’ [ 44 ], ‘social support at the community level’ [ 47 ], ‘community connectedness’ [ 43 ] and ‘sense of community’ [ 53 ]. Table 1 provides additional details on this.

How is CR and SC measured or quantified in research?

The measures used to examine CR and SC are presented Table 1 . It is apparent that there is no uniformity in how SC or CR is measured across the research. Multiple measures are used throughout the retained studies, and nearly all are unique. Additionally, SC was examined at multiple different levels (e.g. cognitive and structural, bonding and bridging), and in multiple different forms (e.g. community connectedness, community cohesion).

What is the association between CR and SC on mental wellbeing?

To best compare research, the following section reports on CR, and facets of SC separately. Please see Supplementary file 4 for additional information on retained papers methods of measuring mental wellbeing.

Community resilience

CR relates to the ability of a community to withstand, adapt and permit growth in adverse circumstances due to social structures, networks and interdependencies within the community [ 11 ].

The impact of CR on mental wellbeing was consistently positive. For example, research indicated that there was a positive association between CR and number of common mental health (i.e. anxiety and mood) treatments post-disaster [ 56 ]. Similarly, other research suggests that CR is positively related to psychological resilience, which is inversely related to depressive symptoms) [ 37 ]. The same research also concluded that CR is protective of psychological resilience and is therefore protective of depressive symptoms [ 37 ].

Social capital

SC reflects the strength of a social network, community reciprocity, and trust in people and institutions [ 14 ]. These aspects of community are usually conceptualised primarily as protective factors that enable communities to cope and adapt collectively to threats.

There were inconsistencies across research which examined the impact of abstract SC (i.e. not refined into bonding/bridging or structural/cognitive) on mental wellbeing. However, for the majority of cases, research deems SC to be beneficial. For example, research has concluded that, SC is protective against post-traumatic stress disorder [ 55 ], anxiety [ 46 ], psychological distress [ 50 ], and stress [ 46 ]. Additionally, SC has been found to facilitate post-traumatic growth [ 38 ], and also to be useful to be drawn upon in times of stress [ 52 ], both of which could be protective of mental health. Similarly, research has also found that emotional recovery following a disaster is more difficult for those who report to have low levels of SC [ 51 ].

Conversely, however, research has also concluded that when other situational factors (e.g. personal resources) were controlled for, a positive relationship between community resources and life satisfaction was no longer significant [ 60 ]. Furthermore, some research has concluded that a high level of SC can result in a community facing greater stress immediately post disaster. Indeed, one retained paper found that high levels of SC correlate with higher levels of post-traumatic stress immediately following a disaster [ 39 ]. However, in the later stages following a disaster, this relationship can reverse, with SC subsequently providing an aid to recovery [ 41 ]. By way of explanation, some researchers have suggested that communities with stronger SC carry the greatest load in terms of helping others (i.e. family, friends and neighbours) as well as themselves immediately following the disaster, but then as time passes the communities recover at a faster rate as they are able to rely on their social networks for support [ 41 ].

Cognitive and structural social capital

Cognitive SC refers to perceptions of community relations, such as trust, mutual help and attachment, and structural SC refers to what actually happens within the community, such as participation, socialising [ 16 ].

Cognitive SC has been found to be protective [ 49 ] against PTSD [ 54 , 57 ], depression [ 40 , 54 ]) mild mood disorder; [ 48 ]), anxiety [ 48 , 54 ] and increase self-efficacy [ 59 ].

For structural SC, research is again inconsistent. On the one hand, structural SC has been found to: increase perceived self-efficacy, be protective of depression [ 40 ], buffer the impact of housing damage on cognitive decline [ 42 ] and provide support during disasters and over the recovery period [ 59 ]. However, on the other hand, it has been found to have no association with PTSD [ 54 , 57 ] or depression, and is also associated with a higher prevalence of anxiety [ 54 ]. Similarly, it is also suggested by additional research that structural SC can harm women’s mental health, either due to the pressure of expectations to help and support others or feelings of isolation [ 49 ].

Bonding and bridging social capital

Bonding SC refers to connections among individuals who are emotionally close, and result in bonds to a particular group [ 17 ], and bridging SC refers to acquaintances or individuals loosely connected that span different social groups [ 18 ].

One research study concluded that both bonding and bridging SC were protective against post-traumatic stress disorder symptoms [ 58 ]. Bridging capital was deemed to be around twice as effective in buffering against post-traumatic stress disorder than bonding SC [ 58 ].

Other community variables

Community social cohesion was significantly associated with a lower risk of post-traumatic stress disorder symptom development [ 35 ], and this was apparent even whilst controlling for depressive symptoms at baseline and disaster impact variables (e.g. loss of family member or housing damage) [ 36 ]. Similarly, sense of community, community connectedness, social support at the community level and neighbourhood connectedness all provided protective benefits for a range of mental health, wellbeing and recovery variables, including: depression [ 53 ], subjective wellbeing (in older adults only) [ 43 ], psychological distress [ 47 ], happiness [ 44 ] and life satisfaction [ 53 ].

Research has also concluded that community level social support is protective against mild mood and anxiety disorder, but only for individuals who have had no previous disaster experience [ 48 ]. Additionally, a study which separated SC into social cohesion and social participation concluded that at a community level, social cohesion is protective against depression [ 49 ] whereas social participation at community level is associated with an increased risk of depression amongst women [ 49 ].

What is the impact of Infectious disease outbreaks / disasters and emergencies on community resilience?

From a cross-sectional perspective, research has indicated that disasters and emergencies can have a negative effect on certain types of SC. Specifically, cognitive SC has been found to be impacted by disaster impact, whereas structural SC has gone unaffected [ 45 ]. Disaster impact has also been shown to have a negative effect on community relationships more generally [ 52 ].

Additionally, of the eight studies which collected data at multiple time points [ 35 , 36 , 41 , 42 , 47 , 49 , 56 , 60 ], three reported the effect of a disaster on the level of SC within a community [ 40 , 42 , 49 ]. All three of these studies concluded that disasters may have a negative impact on the levels of SC within a community. The first study found that the Deepwater Horizon oil spill had a negative effect on SC and social support, and this in turn explained an overall increase in the levels of depression within the community [ 40 ]. A possible explanation for the negative effect lays in ‘corrosive communities’, known for increased social conflict and reduced social support, that are sometimes created following oil spills [ 40 ]. It is proposed that corrosive communities often emerge due to a loss of natural resources that bring social groups together (e.g., for recreational activities), as well as social disparity (e.g., due to unequal distribution of economic impact) becoming apparent in the community following disaster [ 40 ]. The second study found that SC (in the form of social cohesion, informal socialising and social participation) decreased after the 2011 earthquake and tsunami in Japan; it was suggested that this change correlated with incidence of cognitive decline [ 42 ]. However, the third study reported more mixed effects based on physical circumstances of the communities’ natural environment: Following an earthquake, those who lived in mountainous areas with an initial high level of pre-community SC saw a decrease in SC post disaster [ 49 ]. However, communities in flat areas (which were home to younger residents and had a higher population density) saw an increase in SC [ 49 ]. It was proposed that this difference could be due to the need for those who lived in mountainous areas to seek prolonged refuge due to subsequent landslides [ 49 ].

What types of intervention enhance CR and SC and protect survivors?

There were mixed effects across the 26 retained papers when examining the effect of CR and SC on mental wellbeing. However, there is evidence that an increase in SC [ 56 , 57 ], with a focus on cognitive SC [ 57 ], namely by: building social networks [ 45 , 51 , 53 ], enhancing feelings of social cohesion [ 35 , 36 ] and promoting a sense of community [ 53 ], can result in an increase in CR and potentially protect survivors’ wellbeing and mental health following a disaster. An increase in SC may also aid in decreasing the need for individual psychological interventions in the aftermath of a disaster [ 55 ]. As a result, recommendations and suggested methods to bolster CR and SC from the retained papers have been extracted and separated into general methods, preparedness and policy level implementation.

General methods

Suggested methods to build SC included organising recreational activity-based groups [ 44 ] to broaden [ 51 , 53 ] and preserve current social networks [ 42 ], introducing initiatives to increase social cohesion and trust [ 51 ], and volunteering to increase the number of social ties between residents [ 59 ]. Research also notes that it is important to take a ‘no one left behind approach’ when organising recreational and social community events, as failure to do so could induce feelings of isolation for some members of the community [ 49 ]. Furthermore, gender differences should also be considered as research indicates that males and females may react differently to community level SC (as evidence suggests males are instead more impacted by individual level SC; in comparison to women who have larger and more diverse social networks [ 49 ]). Therefore, interventions which aim to raise community level social participation, with the aim of expanding social connections and gaining support, may be beneficial [ 42 , 47 ].

Preparedness

In order to prepare for disasters, it may be beneficial to introduce community-targeted methods or interventions to increase levels of SC and CR as these may aid in ameliorating the consequences of a public health emergency or disaster [ 57 ]. To indicate which communities have low levels of SC, one study suggests implementing a 3-item scale of social cohesion to map areas and target interventions [ 42 ].

It is important to consider that communities with a high level of SC may have a lower level of risk perception, due to the established connections and supportive network they have with those around them [ 61 ]. However, for the purpose of preparedness, this is not ideal as perception of risk is a key factor when seeking to encourage behavioural adherence. This could be overcome by introducing communication strategies which emphasise the necessity of social support, but also highlights the need for additional measures to reduce residual risk [ 59 ]. Furthermore, support in the form of financial assistance to foster current community initiatives may prove beneficial to rural areas, for example through the use of an asset-based community development framework [ 52 ].

Policy level

At a policy level, the included papers suggest a range of ways that CR and SC could be bolstered and used. These include: providing financial support for community initiatives and collective coping strategies, (e.g. using asset-based community development [ 52 ]); ensuring policies for long-term recovery focus on community sustainable development (e.g. community festival and community centre activities) [ 44 ]; and development of a network amongst cooperative corporations formed for reconstruction and to organise self-help recovery sessions among residents of adjacent areas [ 58 ].

This scoping review sought to synthesise literature concerning the role of SC and CR during public health emergencies and disasters. Specifically, in this review we have examined: the methods used to measure CR and SC; the impact of CR and SC on mental wellbeing during disasters and emergencies; the impact of disasters and emergencies on CR and SC; and the types of interventions which can be used to enhance CR. To do this, data was extracted from 26 peer-reviewed journal articles. From this synthesis, several key themes have been identified, which can be used to develop guidelines and recommendations for deploying CR and SC in a public health emergency or disaster context. These key themes and resulting recommendations are summarised below.

Firstly, this review established that there is no consistent or standardised approach to measuring CR or SC within the general population. This finding is consistent with a review conducted by the World Health Organization which concludes that despite there being a number of frameworks that contain indicators across different determinants of health, there is a lack of consensus on priority areas for measurement and no widely accepted indicator [ 27 ]. As a result, there are many measures of CR and SC apparent within the literature (e.g., [ 62 , 63 ]), an example of a developed and validated measure is provided by Sherrieb, Norris and Galea [ 64 ]. Similarly, the definitions of CR and SC differ widely between researchers, which created a barrier to comparing and summarising information. Therefore, future research could seek to compare various interpretations of CR and to identify any overlapping concepts. However, a previous systemic review conducted by Patel et al. (2017) concludes that there are nine core elements of CR (local knowledge, community networks and relationships, communication, health, governance and leadership, resources, economic investment, preparedness, and mental outlook), with 19 further sub-elements therein [ 30 ]. Therefore, as CR is a multi-dimensional construct, the implications from the findings are that multiple aspects of social infrastructure may need to be considered.

Secondly, our synthesis of research concerning the role of CR and SC for ensuring mental health and wellbeing during, or following, a public health emergency or disaster revealed mixed effects. Much of the research indicates either a generally protective effect on mental health and wellbeing, or no effect; however, the literature demonstrates some potential for a high level of CR/SC to backfire and result in a negative effect for populations during, or following, a public health emergency or disaster. Considered together, our synthesis indicates that cognitive SC is the only facet of SC which was perceived as universally protective across all retained papers. This is consistent with a systematic review which also concludes that: (a) community level cognitive SC is associated with a lower risk of common mental disorders, while; (b) community level structural SC had inconsistent effects [ 65 ].

Further examination of additional data extracted from studies which found that CR/SC had a negative effect on mental health and wellbeing revealed no commonalities that might explain these effects (Please see Supplementary file 5 for additional information)

One potential explanation may come from a retained paper which found that high levels of SC result in an increase in stress level immediately post disaster [ 41 ]. This was suggested to be due to individuals having greater burdens due to wishing to help and support their wide networks as well as themselves. However, as time passes the levels of SC allow the community to come together and recover at a faster rate [ 41 ]. As this was the only retained paper which produced this finding, it would be beneficial for future research to examine boundary conditions for the positive effects of CR/SC; that is, to explore circumstances under which CR/SC may be more likely to put communities at greater risk. This further research should also include additional longitudinal research to validate the conclusions drawn by [ 41 ] as resilience is a dynamic process of adaption.

Thirdly, disasters and emergencies were generally found to have a negative effect on levels of SC. One retained paper found a mixed effect of SC in relation to an earthquake, however this paper separated participants by area in which they lived (i.e., mountainous vs. flat), which explains this inconsistent effect [ 49 ]. Dangerous areas (i.e. mountainous) saw a decrease in community SC in comparison to safer areas following the earthquake (an effect the authors attributed to the need to seek prolonged refuge), whereas participants from the safer areas (which are home to younger residents with a higher population density) saw an increase in SC [ 49 ]. This is consistent with the idea that being able to participate socially is a key element of SC [ 12 ]. Overall, however, this was the only retained paper which produced a variable finding in relation to the effect of disaster on levels of CR/SC.

Finally, research identified through our synthesis promotes the idea of bolstering SC (particularly cognitive SC) and cohesion in communities likely to be affected by disaster to improve levels of CR. This finding provides further understanding of the relationship between CR and SC; an association that has been reported in various articles seeking to provide conceptual frameworks (e.g., [ 66 , 67 ]) as well as indicator/measurement frameworks [ 27 ]. Therefore, this could be done by creating and promoting initiatives which foster SC and create bonds within the community. Papers included in the current review suggest that recreational-based activity groups and volunteering are potential methods for fostering SC and creating community bonds [ 44 , 51 , 59 ]. Similarly, further research demonstrates that feelings of social cohesion are enhanced by general social activities (e.g. fairs and parades [ 18 ]). Also, actively encouraging activities, programs and interventions which enhance connectedness and SC have been reported to be desirable to increase CR [ 68 ]. This suggestion is supported by a recent scoping review of literature [ 67 ] examined community champion approaches for the COVID-19 pandemic response and recovery and established that creating and promoting SC focused initiatives within the community during pandemic response is highly beneficial [ 67 ]. In terms of preparedness, research states that it may be beneficial for levels of SC and CR in communities at risk to be assessed, to allow targeted interventions where the population may be at most risk following an incident [ 42 , 44 ]. Additionally, from a more critical perspective, we acknowledge that ‘resilience’ can often be perceived as a focus on individual capacity to adapt to adversity rather than changing or mitigating the causes of adverse conditions [ 69 , 70 ]. Therefore, CR requires an integrated system approach across individual, community and structural levels [ 17 ]. Also, it is important that community members are engaged in defining and agreeing how community resilience is measured [ 27 ] rather than it being imposed by system leads or decision-makers.

In the aftermath of the pandemic, is it expected that there will be long-term repercussions both from an economic [ 8 ] and a mental health perspective [ 71 ]. Furthermore, the findings from this review suggest that although those in areas with high levels of SC may be negatively affected in the acute stage, as time passes, they have potential to rebound at a faster rate than those with lower levels of SC. Ongoing evaluation of the effectiveness of current initiatives as the COVID-19 pandemic progresses into a recovery phase will be invaluable for supplementing the evidence base identified through this review.

Recommendations

As a result of this review, a number of recommendations are suggested for policy and practice during public health emergencies and recovery.

Future research should seek to establish a standardised and validated approach to measuring and defining CR and SC within communities. There are ongoing efforts in this area, for example [ 72 ]. Additionally, community members should be involved in the process of defining how CR is measured.

There should be an enhanced effort to improve preparedness for public health emergencies and disasters in local communities by gauging current levels of SC and CR within communities using a standardised measure. This approach could support specific targeting of populations with low levels of CR/SC in case of a disaster or public health emergency, whilst also allowing for consideration of support for those with high levels of CR (as these populations can be heavily impacted initially following a disaster). By distinguishing levels of SC and CR, tailored community-centred approaches could be implemented, such as those listed in a guide released by PHE in 2015 [ 73 ].

CR and SC (specifically cognitive SC) should be bolstered if communities are at risk of experiencing a disaster or public health emergency. This can be achieved by using interventions which aim to increase a sense of community and create new social ties (e.g., recreational group activities, volunteering). Additionally, when aiming to achieve this, it is important to be mindful of the risk of increased levels of CR/SC to backfire, as well as seeking to advocate an integrated system approach across individual, community and structural levels.

It is necessary to be aware that although communities with high existing levels of resilience / SC may experience short-term negative consequences following a disaster, over time these communities might be able to recover at a faster rate. It is therefore important to ensure that suitable short-term support is provided to these communities in the immediate aftermath of a public health emergency or disaster.

Robust evaluation of the community resilience initiatives deployed during the COVID-19 pandemic response is essential to inform the evidence base concerning the effectiveness of CR/ SC. These evaluations should continue through the response phase and into the recovery phase to help develop our understanding of the long-term consequences of such interventions.

Limitations

Despite this review being the first in this specific topic area, there are limitations that must be considered. Firstly, it is necessary to note that communities are generally highly diverse and the term ‘community’ in academic literature is a subject of much debate (see: [ 74 ]), therefore this must be considered when comparing and collating research involving communities. Additionally, the measures of CR and SC differ substantially across research, including across the 26 retained papers used in the current review. This makes the act of comparing and collating research findings very difficult. This issue is highlighted as a key outcome from this review, and suggestions for how to overcome this in future research are provided. Additionally, we acknowledge that there will be a relationship between CR & SC even where studies measure only at individual or community level. A review [ 75 ] on articulating a hypothesis of the link to health inequalities suggests that wider structural determinants of health need to be accounted for. Secondly, despite the final search strategy encompassing terms for both CR and SC, only one retained paper directly measured CR; thus, making the research findings more relevant to SC. Future research could seek to focus on CR to allow for a comparison of findings. Thirdly, the review was conducted early in the COVID-19 pandemic and so does not include more recent publications focusing on resilience specifically in the context of COVID-19. Regardless of this fact, the synthesis of, and recommendations drawn from, the reviewed studies are agnostic to time and specific incident and contain critical elements necessary to address as the pandemic moves from response to recovery. Further research should review the effectiveness of specific interventions during the COVID-19 pandemic for collation in a subsequent update to this current paper. Fourthly, the current review synthesises findings from countries with individualistic and collectivistic cultures, which may account for some variation in the findings. Lastly, despite choosing a scoping review method for ease of synthesising a wide literature base for use by public health emergency researchers in a relatively tight timeframe, there are disadvantages of a scoping review approach to consider: (1) quality appraisal of retained studies was not carried out; (2) due to the broad nature of a scoping review, more refined and targeted reviews of literature (e.g., systematic reviews) may be able to provide more detailed research outcomes. Therefore, future research should seek to use alternative methods (e.g., empirical research, systematic reviews of literature) to add to the evidence base on CR and SC impact and use in public health practice.

This review sought to establish: (1) How CR and SC are quantified in research?; (2) The impact of community resilience on mental wellbeing?; (3) The impact of infectious disease outbreaks, disasters and emergencies on community resilience and social capital?; and, (4) What types of interventions enhance community resilience and social capital?. The chosen search strategy yielded 26 relevant papers from which we were able extract information relating to the aims of this review.

Results from the review revealed that CR and SC are not measured consistently across research. The impact of CR / SC on mental health and wellbeing during emergencies and disasters is mixed (with some potential for backlash), however the literature does identify cognitive SC as particularly protective. Although only a small number of papers compared CR or SC before and after a disaster, the findings were relatively consistent: SC or CR is negatively impacted by a disaster. Methods suggested to bolster SC in communities were centred around social activities, such as recreational group activities and volunteering. Recommendations for both research and practice (with a particular focus on the ongoing COVID-19 pandemic) are also presented.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Social Capital

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This study was supported by the National Institute for Health Research Research Unit (NIHR HPRU) in Emergency Preparedness and Response, a partnership between Public Health England, King’s College London and the University of East Anglia. The views expressed are those of the author(s) and not necessarily those of the NIHR, Public Health England, the UK Health Security Agency or the Department of Health and Social Care [Grant number: NIHR20008900]. Part of this work has been funded by the Office for Health Improvement and Disparities, Department of Health and Social Care, as part of a Collaborative Agreement with Leeds Beckett University.

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Hall, C.E., Wehling, H., Stansfield, J. et al. Examining the role of community resilience and social capital on mental health in public health emergency and disaster response: a scoping review. BMC Public Health 23 , 2482 (2023). https://doi.org/10.1186/s12889-023-17242-x

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I tried the new Google. Its answers are worse.

Google’s ai-‘supercharged’ search generative experience, or sge, sometimes makes up facts, misinterprets questions and picks low-quality sources — even after nearly 11 months of public testing..

Have you heard about the new Google ? They “ supercharged ” it with artificial intelligence. Somehow, that also made it dumber.

With the regular old Google, I can ask, “What’s Mark Zuckerberg’s net worth?” and a reasonable answer pops up: “169.8 billion USD.”

Now let’s ask the same question with the “experimental” new version of Google search. Its AI responds: Zuckerberg’s net worth is “$46.24 per hour, or $96,169 per year. This is equivalent to $8,014 per month, $1,849 per week, and $230.6 million per day.”

Um, none of those numbers add up.

Google acting dumb matters because its AI is headed to your searches sooner or later . The company has already been testing this new Google — dubbed Search Generative Experience, or SGE — with volunteers for nearly 11 months, and recently started showing AI answers in the main Google results even for people who have not opted in to the test .

Should you trust that AI?

The new Google can do some useful things. But as you’ll see, it sometimes also makes up facts, misinterprets questions, delivers out-of-date information and just generally blathers on. Even worse, researchers are finding the AI often elevates lower-quality sites as reliable sources of information.

Normally, I wouldn’t review a product that isn’t finished. But this test of Google’s future has been going on for nearly a year, and the choices being made now will influence how billions of people get information. At stake is also a core idea behind the current AI frenzy: that the tech can replace the need to research things ourselves by just giving us answers. If a company with the money and computing power of Google can’t make it work, who can?

SGE merges the search engine you know with the capabilities of a chatbot. On top of traditional results, SGE writes out direct answers to queries, interspersed with links to dig deeper.

Geoffrey A. Fowler

SGE is a response to the reality that some people, including me, are starting to turn to AI like ChatGPT for more complex questions or when we don’t feel like reading a bunch of different sites. Onely , a search optimization firm, estimates that using SGE can make a user’s overall research journey 10 to 20 times shorter by assembling pros and cons, prices and other information into one place.

An all-knowing answer bot sounds useful given our shrinking attention spans. But Google has a lot to work out. We expect searches to be fast, yet Google’s AI answers take a painful second or two to generate. Google has to balance the already fragile economy of the web, where its AI answers can steal traffic from publishers who do the expensive and hard work of actually researching things.

And most of all, the new Google has to deliver on the promise that it can consistently and correctly answer our questions. That’s where I focused my testing — and kept finding examples where the AI-supercharged Google did worse than its predecessor.

Putting Google’s AI answers to the test

Often when you’re Googling, what you really want is a short bit of information or a link. On a day-to-day basis, the new Google is often annoying because its AI is so darned chatty.

A goofy example: “What do Transformers eat?”

The AI answer told me that fictional robots don’t really need to eat or drink, though they need some kind of fuel. Meanwhile, old Google had the one-word answer I was looking for: Energon. (It’s a kind of magical fuel.) You got that answer from new Google only by scrolling down the page.

This doesn’t just happen with alien robots. When SE Ranking, a firm dedicated to search engine optimization, tested SGE with 100,000 keyword queries, it found the average answer it generated was 3,485 characters — or roughly a third as long as this column. One of Google’s challenges is figuring out when its AI is better off just keeping quiet; sometimes, SGE asks you to press a “generate” button before it will write out an answer.

Most of all, when we search, we expect correct information. Google claims SGE has a leg up on ChatGPT because its knowledge is up-to-date.

Yet I found the new Google still struggled with recent affairs. Three days after the most recent Academy Awards, I searched for “Oscars 2024.” It told me the Oscars were still to come and listed some nominees.

And nothing undermined my trust in Google’s AI answers more than watching it confidently make stuff up.

That includes facts about yours truly. I asked it about an award-winning series I wrote for The Washington Post, and it attributed it to some stranger — and then gave a link to some other website.

Then there was the time SGE all too happily made up information about something that doesn’t even exist. I asked about a San Francisco restaurant called Danny’s Dan Dan Noodles, and it told me it has “crazy wait times” and described its food.

The problem is that this is an imaginary shop I named after my favorite Chinese dish. Google’s AI had no problem inventing information about it.

So-called hallucinations about real and fake topics are a known problem with current AI. A disclaimer above SGE results says, “Generative AI is experimental,” but that doesn’t solve the problem. Google needs to figure out how to say “I don’t know” when it isn’t confident.

Suspect sources

To give us answers to everything, Google’s AI has to decide which sources are reliable. I’m not very confident about its judgment.

Remember our bonkers result on Zuckerberg’s net worth? A professional researcher — and also regular old Google — might suggest checking the billionaires list from Forbes . Google’s AI answer relied on a very weird ZipRecruiter page for “Mark Zuckerberg Jobs,” a thing that does not exist.

In my tests, suspect sources were a pattern. At the suggestion of Onely, I asked the new Google which was more reliable: Apple iPhones or Samsung phones. As a longtime reviewer, I could tell you lots of good sources of information on this, including professional journalists and repair organizations like iFixit.

Instead, the AI cites random views of people pulled from social media. Beyond the limited usefulness of a single Reddit user’s experience, how does Google know that it wasn’t a fake review posted by the phonemaker?

“Google SGE plays by a different set of rules compared to the traditional search engine we know today,” said Tomek Rudzki, Onely’s head of research and development.

SEO firms have been trying to do quantitative studies of SGE’s values, though they’re limited by Google’s requirements on test accounts. But they’ve found a similar pattern in the disconnect between the sites that the old and new Google link to. The SEO software company Authoritas tested searches with a thousand shopping terms in late March, and found that 77 percent of the time, the domain of the No. 1 traditional search result showed up nowhere in the AI-written answer.

And in its study of 100,000 keyword searches, SE Ranking found that the question-and-answer service Quora is the most-linked source by SGE; LinkedIn and Reddit were fifth and sixth. How often would those sources be acceptable on an eighth-grade term paper?

On searches about tech topics — including lots of “how to” questions — SE Ranking found the most-linked domain was simplilearn.com . I’d never heard of it before; the site describes itself as an “online bootcamp.”

“This trend not only diminishes the quality of search results but also reduces traffic and revenue for many small businesses, including affiliate websites,” says SE Ranking’s head of SEO, Anastasia Kotsiubynska.

A work in progress

Google says SGE is an opt-in experiment. But Google already blew past its expected end last December, and it hasn’t offered any update on when it will come to search for everyone. It’s possible that Google doesn’t think SGE is accurate or fast or profitable enough and that it will end up changing it dramatically.

They are wise to go slow, even if it makes Google look as though it’s behind in the AI race. The rival search engine Bing from Microsoft made a similar AI overhaul in February 2023, but its AI is still best known for going off the rails .

In an interview, Elizabeth Reid, a Google vice president leading SGE, characterized it as a work in progress.

“We’re really focused on ensuring we get the experience really right. There are a lot of different factors on this — things like latency, accuracy, helpfulness,” Reid said. “What we’ve been finding as we’re iterating and learning is that it’s pretty nuanced.” In other words, there are times the AI is helpful and other times it’s not — and Google is still trying to figure out where to draw the line.

When I shared the examples in this column, Reid told me that SGE’s hallucination rates are “very low” and have decreased “meaningfully” since SGE’s May launch, though she declined to be specific.

“I don’t want to minimize it — it is a challenge with the technology” and something “we’re really working on,” Reid said. Putting links right next to the AI answers, she added, is important to enable people to check the facts for themselves.

Here’s a proposal: Because Google acknowledges correct facts are a problem, it ought to disclose its own data on accuracy before it brings SGE to a broader audience. With billions of searches daily, even 0.001 percent can add up to a lot of wrong information.

Another area of Google’s focus is “trying to help ensure that we get to the core of the question as quickly as possible, and then give additional elaboration,” Reid said.

As for citing low-quality sources, Google disputed the outside research on SGE, saying it is based on searches that are more limited than what Google sees in practice. But it declined to share data of its own.

Reid said SGE doesn’t have a different standard than old Google. “We do see more diversity of sources that are coming forth. But the aim is really to continue to put high-quality content at the top,” she said.

Choosing who to believe is hard enough for humans. What makes Google think its current AI tech, known as LLMs, or large language models, is up to the task?

“They’re not perfect,” Reid said. “We want to take this thoughtful approach because the brand of trust that people have with Google is really important.”

The future of our information depends on it.

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The Best Time to Sell in 2024: The Week of April 14-20

Home sellers who are hoping to sell this year and looking for the perfect time to list should start getting ready—because the best time to list a home in 2024 is approaching quickly.

The week of April 14–20 is expected to have the ideal balance of housing market conditions that favor home sellers, more so than any other w eek in the year. A recent survey from Realtor.com ® found that the majority (53%) of home sellers took one month or less to get their home ready to list, so the time to start prepping is now.

This selection comes from looking at seasonal trends from 2018–19 and 2021–23 data and calculating a Best Time to List score for each week of the year, based on a combination of housing metrics. Notably, mortgage rates are not included in the score as mortgage rate movement has more to do with the larger economic context, and not seasonal shifts.

The State of the Housing Market

We expect the 2024 housing market to behave according to typical seasonality, but offer slightly better conditions than 2023. Each week was scored based on favorability toward sellers—this included competition from other sellers (active listings and new listings), listing prices, market pace (days on the market), likelihood of price reductions, and homebuyer demand (views per property on Realtor.com). Percentile levels for each week were calculated along each metric and were then averaged together across metrics to determine a Best Time to List score. Rankings for each week were based on these Best Time to List scores.

2023 was a fairly glum year in housing, with prices remaining near record high levels while inventory levels suffered. Mortgage rates started the year in the mid-6% range and climbed to nearly 8% in October , continuing to weigh down the affordability of housing payments despite unremarkable price growth.

Home prices peaked at a median listing price of $445,000 nationally in June 2023 , falling short of the previous year’s all-time high. Though prices did not reach a new peak this year, they remained near year-ago levels, failing to offer much relief to buyers. Buyer demand remained stifled as home shoppers took a step back amid high prices, elevated mortgage rates, and low inventory.

Though low housing demand set the tone for much of 2023, homes still spent significantly less time on the market than before the COVID-19 pandemic, and inventory remained well below pre-pandemic norms.

Many homeowners felt “locked in” by their current mortgage , hesitant to list their home for sale and trade a sub-4% mortgage for a 7%-plus mortgage, which kept new listing activity low for much of the year.

Builders slowed new construction activity slightly in 2023 amid low buyer demand and economic uncertainty . Both single- and multi-family housing starts fell relative to the previous few years, but both remained above pre-pandemic levels as builders aimed to fill some of the gap left by low existing-home inventory. Though starts waned, new-home completions climbed relative to the previous year, supplying much sought-after inventory for buyers and renters alike.

Mortgage rates fell quickly toward the end of the year as the Fed signaled that rate cuts were likely for 2024 , and as a result, both buyer and seller activity ticked up slightly heading into the new year.

In February, new listing activity climbed 11.3% , resulting in 14.8% more for-sale inventory in the month than one year prior. Though selling activity has picked up, inventory remains nearly 40% below pre-pandemic levels, making it a good time to be a seller today . While some homebuyers are waiting for mortgage rates to fall further before entering the housing market, it’s still a good time for homeowners to sell as buyers continue to need more for-sale options.

Benefits of listing a home the week of April 14–20, 2024

At a national level, this week represents a balanced selection of market conditions that favor sellers. While it does not have the highest price or the lowest time on the market of the year, this week offers higher-than-average prices and lower-than-average time on the market while also offering a higher-than-average number of buyers—measured as viewers per listing.

While affordability will continue to be a challenge for buyers and sellers who are looking to buy, we expect lower mortgage rates and more new-construction inventory to offer some relief and inject some life back into the market. In more balanced housing market conditions, we expect the benefits of strategically listing during the most seasonally advantageous week to be greater.

Above-average prices: Homes during this week have historically reached prices 1.1% higher than the average week throughout the year, and are typically 10.4% higher than the start of the year. This year is likely to look a lot like 2023 due to still-high housing costs. If 2024 follows the 2023 seasonal trend, the national median listing price could reach $7,400 above the average week, and $34,000 more than the start of the year.

Above-average buyer demand: The number of buyers looking at a listing can determine how many offers a home gets and how quickly it sells. The more buyers looking at a home, the better for the seller, and in most years, buyers start earlier than sellers.

Historically, this week garnered 18.4% more views per listing than the typical week. However, in 2023, this week got 22.8% more views per listing than the average week throughout the year. If mortgage rates fall more significantly this spring, it is possible that demand will surge sooner. However, if mortgage rates don’t soften until later in the year, then buyers may hold off in hopes of lower rates.

According to the February Fannie Mae housing survey , a near-all-time survey-high 35% of respondents indicated that they expect mortgage rates to go down in the next 12 months. After climbing through February, mortgage rates eased in the latest data. Mortgage rate expectations could lead more buyers to hold off until mortgage rates fall further, which may mean a slower ramp-up in demand this spring.

Fast market pace: Thanks to above-average demand, homes sell more quickly during this week.

Historically, homes actively for sale during this week sold 17%, or roughly 9 days, faster than the average week. In the 2023 market, this week saw homes typically on the market for 46 days, 6 days faster than the year’s average and 7 days faster than was typical in 2019. The 2023 market moved more slowly than the previous few years due to affordability challenges, but the market pace picked up toward the end of the year and into 2024 as easing mortgage rates stoked buyer demand. If inventory levels remain relatively low, time on the market may pick up faster as buyers vie for fewer homes.

Lower competition from other sellers: A typical inventory trend would mean 13.7% fewer sellers on the market during this week compared with the average week throughout the year. With few exceptions, the number of sellers tends to increase from the beginning of the year until roughly November. Last year saw more significant inventory gains after the first four months as buyer demand cooled, but sellers responded by pulling back on listings once again by the end of the year. Active inventory was 7.9% higher at the start of 2024 versus 2023 with the highest beginning-of-year inventory since 2020 . However, inventory was still 39.7% lower than pre-pandemic levels. This gap means there continue to be opportunities for sellers who enter the market this spring.

Below-average price reductions: Price reductions tend to peak in the fall as sellers left on the market after the summer rush try to attract attention. Price reductions tend to be the lowest from late winter into spring as buyer activity ramps up.

During the Best Week, roughly 24.6% fewer homes have had a price reduction than the average week of the year, which translates to a 0.9 percentage point lower price-reduced share compared with the average week of the year. In 2023, this week saw roughly 8,000 fewer listings with price reductions than the average week of the year.

Market dynamics shift—baby steps toward affordability

The 2023 housing market continued the slowdown seen in the second half of 2022. Home sales fell to the lowest level in over a decade as buyers struggled with still-high home prices and elevated mortgage rates. Sellers largely kept to the sidelines, hesitant to trade their existing mortgage for one with a much higher interest rate.

Though buyer demand waned, low for-sale inventory meant that buyers had fewer choices and faced competition in many markets, especially more affordable locales . As mortgage rates improved at the very end of the year, buyer demand picked up, indicating that home shoppers are eagerly awaiting a more affordable housing market.

Based on a recent survey , 40% of prospective buyers would find a home purchase feasible if rates were below 6%, and 32% would feel the same if rates fell below 5%.

Mortgage rates are expected to ease into the mid-6% range. Mortgage rates reached as high as 7.79% in 2023 before falling into the mid-to-high 6% range by the end of the year. We expect mortgage rates to remain in this range until incoming economic data suggests that inflation is slowing toward the 2% target level. To date, both employment and inflation have remained strong, which means that the Federal Open Market Committee is likely to hold off on any cuts to the federal funds rate until later in the year. Once rate cuts seem probable, mortgage rates are likely to ease.
Prices tend to peak later, as does competition. Sellers should consider that peak prices later in the season also come with greater competition from other sellers for a similar-sized pool of buyers. Historically, by the end of June, while prices reached near-peak levels (+13.8%) compared with the start of the year, new sellers also surged, increasing to nearly 1.5 times higher than at the start of the year (+49.3%). More sellers mean more options for buyers and therefore more competition among sellers . Sellers can mitigate that risk by being an early entrant into the market, raising their already high odds of a successful close and likely negotiating favorable terms.
Level of buyer and seller activity will be fairly dependent on mortgage rates. Many homeowners are hesitant to enter today’s housing market since their current mortgage rate is much lower than today’s prevailing rate. However, buyers are likely to return to the housing market eagerly upon mortgage rate improvements, which means sellers still stand to see favorable buyer attention on their home listing due to low inventory. While overall buyer demand may not be what it was in the past couple of years, many areas are still seeing competition for homes due to low inventory levels.

What does this mean for sellers?

While we’ve identified april 14–20 as the best week to list for sellers, the housing market remains undersupplied, so a seller listing a well-priced, move-in ready home is likely to find success., because spring is generally the high season for real estate activity and buyers are more plentiful earlier rather than later in the year, listing earlier in the spring raises a seller’s odds of a successful sale. sellers will want to remember that it’s a process and get started well before their intended listing date. recent realtor.com survey data shows that sellers typically took between a couple of weeks to a couple of months to prepare and list their home for sale., what does this mean for homebuyers.

For buyers who have been facing still-high home prices and elevated mortgage rates, there is a key takeaway: The usual seasonal dynamics of the housing market, builder sentiment, and general economic shifts suggest that it’s going to get better.

Inventory improved in late 2023, though levels remain below pre-pandemic levels. So far in 2024, new single-family construction activity and homebuilder sentiment have remained steady, and home completions have remained strong, suggesting that new inventory is likely to provide buyers more options into the spring.

Historically, the number of views per listing has cooled in the late summer/early fall and tends to improve for buyers from that point forward. Additionally, by mid-August, the number of sellers with actively listed homes increased 29% over the beginning of the year, which means more options for buyers . Thus, buyers who can persist in their home searches are likely to catch a bit of a break in the sense that they can expect some more options to choose from in the weeks ahead.

Best Time to List—50 Largest Metro Areas

Methodology.

Listing metrics (e.g., list prices) from 2018–19 and 2021–23 were measured on a weekly basis, with each week compared against a benchmark from the first full week of the year. Due to the onset of the COVID-19 pandemic, 2020 was an uncharacteristic year and has therefore been excluded from the analysis. Averaging across the years yielded the “typical” seasonal trend for each metric. Percentile levels for each week were calculated along each metric (prices, listings, days on the market, etc.) and then averaged together across metrics to determine a Best Time to List score for each week. Rankings for each week were based on these Best Time to List scores.

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Background: The use of surveillance technologies is becoming increasingly common in inpatient mental health settings, commonly justified as efforts to improve safety and cost-effectiveness. However, the use of these technologies has been questioned in light of limited research conducted and the sensitivities, ethical concerns and potential harms of surveillance. This systematic review aims to: 1) map how surveillance technologies have been employed in inpatient mental health settings, 2) identify any best practice guidance, 3) explore how they are experienced by patients, staff and carers, and 4) examine evidence regarding their impact. Methods: We searched five academic databases (Embase, MEDLINE, PsycInfo, PubMed and Scopus), one grey literature database (HMIC) and two pre-print servers (medRxiv and PsyArXiv) to identify relevant papers published up to 18/09/2023. We also conducted backwards and forwards citation tracking and contacted experts to identify relevant literature. Quality was assessed using the Mixed Methods Appraisal Tool. Data were synthesised using a narrative approach. Results: A total of 27 studies were identified as meeting the inclusion criteria. Included studies reported on CCTV/video monitoring (n = 13), Vision-Based Patient Monitoring and Management (VBPMM) (n = 6), Body Worn Cameras (BWCs) (n = 4), GPS electronic monitoring (n = 2) and wearable sensors (n = 2). Twelve papers (44.4%) were rated as low quality, five (18.5%) medium quality, and ten (37.0%) high quality. Five studies (18.5%) declared a conflict of interest. We identified minimal best practice guidance. Qualitative findings indicate that patient, staff and carer perceptions and experiences of surveillance technologies are mixed and complex. Quantitative findings regarding the impact of surveillance on outcomes such as self-harm, violence, aggression, care quality and cost-effectiveness were inconsistent or weak. Discussion: There is currently insufficient evidence to suggest that surveillance technologies in inpatient mental health settings are achieving the outcomes they are employed to achieve, such as improving safety and reducing costs. The studies were generally of low methodological quality, lacked lived experience involvement, and a substantial proportion (18.5%) declared conflicts of interest. Further independent coproduced research is needed to more comprehensively evaluate the impact of surveillance technologies in inpatient settings, including harms and benefits. If surveillance technologies are to be implemented, it will be important to engage all key stakeholders in the development of policies, procedures and best practice guidance to regulate their use, with a particular emphasis on prioritising the perspectives of patients.

Competing Interest Statement

AS and UF have undertaken and published research on BWCs. We have received no financial support from BWC or any other surveillance technology companies. All other authors declare no competing interests.

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https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=463993

Funding Statement

This study is funded by the National Institute for Health and Care Research (NIHR) Policy Research Programme (grant no. PR-PRU-0916-22003). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ARG was supported by the Ramon y Cajal programme (RYC2022-038556-I), funded by the Spanish Ministry of Science, Innovation and Universities.

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Data Availability

The template data extraction form is available in Supplementary 1. MMAT quality appraisal ratings for each included study are available in Supplementary 2. All data used is publicly available in the published papers included in this review.

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Open access
Published: 26 March 2024

Predicting and improving complex beer flavor through machine learning

Michiel Schreurs ORCID: orcid.org/0000-0002-9449-5619 1 , 2 , 3 na1 ,
Supinya Piampongsant 1 , 2 , 3 na1 ,
Miguel Roncoroni ORCID: orcid.org/0000-0001-7461-1427 1 , 2 , 3 na1 ,
Lloyd Cool ORCID: orcid.org/0000-0001-9936-3124 1 , 2 , 3 , 4 ,
Beatriz Herrera-Malaver ORCID: orcid.org/0000-0002-5096-9974 1 , 2 , 3 ,
Christophe Vanderaa ORCID: orcid.org/0000-0001-7443-5427 4 ,
Florian A. Theßeling 1 , 2 , 3 ,
Łukasz Kreft ORCID: orcid.org/0000-0001-7620-4657 5 ,
Alexander Botzki ORCID: orcid.org/0000-0001-6691-4233 5 ,
Philippe Malcorps 6 ,
Luk Daenen 6 ,
Tom Wenseleers ORCID: orcid.org/0000-0002-1434-861X 4 &
Kevin J. Verstrepen ORCID: orcid.org/0000-0002-3077-6219 1 , 2 , 3

Nature Communications volume 15 , Article number: 2368 ( 2024 ) Cite this article

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Chemical engineering
Gas chromatography
Machine learning
Metabolomics
Taste receptors

The perception and appreciation of food flavor depends on many interacting chemical compounds and external factors, and therefore proves challenging to understand and predict. Here, we combine extensive chemical and sensory analyses of 250 different beers to train machine learning models that allow predicting flavor and consumer appreciation. For each beer, we measure over 200 chemical properties, perform quantitative descriptive sensory analysis with a trained tasting panel and map data from over 180,000 consumer reviews to train 10 different machine learning models. The best-performing algorithm, Gradient Boosting, yields models that significantly outperform predictions based on conventional statistics and accurately predict complex food features and consumer appreciation from chemical profiles. Model dissection allows identifying specific and unexpected compounds as drivers of beer flavor and appreciation. Adding these compounds results in variants of commercial alcoholic and non-alcoholic beers with improved consumer appreciation. Together, our study reveals how big data and machine learning uncover complex links between food chemistry, flavor and consumer perception, and lays the foundation to develop novel, tailored foods with superior flavors.

Sensory lexicon and aroma volatiles analysis of brewing malt

Xiaoxia Su, Miao Yu, … Tianyi Du

Predicting odor from molecular structure: a multi-label classification approach

Kushagra Saini & Venkatnarayan Ramanathan

Toward a general and interpretable umami taste predictor using a multi-objective machine learning approach

Lorenzo Pallante, Aigli Korfiati, … Marco A. Deriu

Introduction

Predicting and understanding food perception and appreciation is one of the major challenges in food science. Accurate modeling of food flavor and appreciation could yield important opportunities for both producers and consumers, including quality control, product fingerprinting, counterfeit detection, spoilage detection, and the development of new products and product combinations (food pairing) 1 , 2 , 3 , 4 , 5 , 6 . Accurate models for flavor and consumer appreciation would contribute greatly to our scientific understanding of how humans perceive and appreciate flavor. Moreover, accurate predictive models would also facilitate and standardize existing food assessment methods and could supplement or replace assessments by trained and consumer tasting panels, which are variable, expensive and time-consuming 7 , 8 , 9 . Lastly, apart from providing objective, quantitative, accurate and contextual information that can help producers, models can also guide consumers in understanding their personal preferences 10 .

Despite the myriad of applications, predicting food flavor and appreciation from its chemical properties remains a largely elusive goal in sensory science, especially for complex food and beverages 11 , 12 . A key obstacle is the immense number of flavor-active chemicals underlying food flavor. Flavor compounds can vary widely in chemical structure and concentration, making them technically challenging and labor-intensive to quantify, even in the face of innovations in metabolomics, such as non-targeted metabolic fingerprinting 13 , 14 . Moreover, sensory analysis is perhaps even more complicated. Flavor perception is highly complex, resulting from hundreds of different molecules interacting at the physiochemical and sensorial level. Sensory perception is often non-linear, characterized by complex and concentration-dependent synergistic and antagonistic effects 15 , 16 , 17 , 18 , 19 , 20 , 21 that are further convoluted by the genetics, environment, culture and psychology of consumers 22 , 23 , 24 . Perceived flavor is therefore difficult to measure, with problems of sensitivity, accuracy, and reproducibility that can only be resolved by gathering sufficiently large datasets 25 . Trained tasting panels are considered the prime source of quality sensory data, but require meticulous training, are low throughput and high cost. Public databases containing consumer reviews of food products could provide a valuable alternative, especially for studying appreciation scores, which do not require formal training 25 . Public databases offer the advantage of amassing large amounts of data, increasing the statistical power to identify potential drivers of appreciation. However, public datasets suffer from biases, including a bias in the volunteers that contribute to the database, as well as confounding factors such as price, cult status and psychological conformity towards previous ratings of the product.

Classical multivariate statistics and machine learning methods have been used to predict flavor of specific compounds by, for example, linking structural properties of a compound to its potential biological activities or linking concentrations of specific compounds to sensory profiles 1 , 26 . Importantly, most previous studies focused on predicting organoleptic properties of single compounds (often based on their chemical structure) 27 , 28 , 29 , 30 , 31 , 32 , 33 , thus ignoring the fact that these compounds are present in a complex matrix in food or beverages and excluding complex interactions between compounds. Moreover, the classical statistics commonly used in sensory science 34 , 35 , 36 , 37 , 38 , 39 require a large sample size and sufficient variance amongst predictors to create accurate models. They are not fit for studying an extensive set of hundreds of interacting flavor compounds, since they are sensitive to outliers, have a high tendency to overfit and are less suited for non-linear and discontinuous relationships 40 .

In this study, we combine extensive chemical analyses and sensory data of a set of different commercial beers with machine learning approaches to develop models that predict taste, smell, mouthfeel and appreciation from compound concentrations. Beer is particularly suited to model the relationship between chemistry, flavor and appreciation. First, beer is a complex product, consisting of thousands of flavor compounds that partake in complex sensory interactions 41 , 42 , 43 . This chemical diversity arises from the raw materials (malt, yeast, hops, water and spices) and biochemical conversions during the brewing process (kilning, mashing, boiling, fermentation, maturation and aging) 44 , 45 . Second, the advent of the internet saw beer consumers embrace online review platforms, such as RateBeer (ZX Ventures, Anheuser-Busch InBev SA/NV) and BeerAdvocate (Next Glass, inc.). In this way, the beer community provides massive data sets of beer flavor and appreciation scores, creating extraordinarily large sensory databases to complement the analyses of our professional sensory panel. Specifically, we characterize over 200 chemical properties of 250 commercial beers, spread across 22 beer styles, and link these to the descriptive sensory profiling data of a 16-person in-house trained tasting panel and data acquired from over 180,000 public consumer reviews. These unique and extensive datasets enable us to train a suite of machine learning models to predict flavor and appreciation from a beer’s chemical profile. Dissection of the best-performing models allows us to pinpoint specific compounds as potential drivers of beer flavor and appreciation. Follow-up experiments confirm the importance of these compounds and ultimately allow us to significantly improve the flavor and appreciation of selected commercial beers. Together, our study represents a significant step towards understanding complex flavors and reinforces the value of machine learning to develop and refine complex foods. In this way, it represents a stepping stone for further computer-aided food engineering applications 46 .

To generate a comprehensive dataset on beer flavor, we selected 250 commercial Belgian beers across 22 different beer styles (Supplementary Fig. S1 ). Beers with ≤ 4.2% alcohol by volume (ABV) were classified as non-alcoholic and low-alcoholic. Blonds and Tripels constitute a significant portion of the dataset (12.4% and 11.2%, respectively) reflecting their presence on the Belgian beer market and the heterogeneity of beers within these styles. By contrast, lager beers are less diverse and dominated by a handful of brands. Rare styles such as Brut or Faro make up only a small fraction of the dataset (2% and 1%, respectively) because fewer of these beers are produced and because they are dominated by distinct characteristics in terms of flavor and chemical composition.

Extensive analysis identifies relationships between chemical compounds in beer

For each beer, we measured 226 different chemical properties, including common brewing parameters such as alcohol content, iso-alpha acids, pH, sugar concentration 47 , and over 200 flavor compounds (Methods, Supplementary Table S1 ). A large portion (37.2%) are terpenoids arising from hopping, responsible for herbal and fruity flavors 16 , 48 . A second major category are yeast metabolites, such as esters and alcohols, that result in fruity and solvent notes 48 , 49 , 50 . Other measured compounds are primarily derived from malt, or other microbes such as non- Saccharomyces yeasts and bacteria (‘wild flora’). Compounds that arise from spices or staling are labeled under ‘Others’. Five attributes (caloric value, total acids and total ester, hop aroma and sulfur compounds) are calculated from multiple individually measured compounds.

As a first step in identifying relationships between chemical properties, we determined correlations between the concentrations of the compounds (Fig. 1 , upper panel, Supplementary Data 1 and 2 , and Supplementary Fig. S2 . For the sake of clarity, only a subset of the measured compounds is shown in Fig. 1 ). Compounds of the same origin typically show a positive correlation, while absence of correlation hints at parameters varying independently. For example, the hop aroma compounds citronellol, and alpha-terpineol show moderate correlations with each other (Spearman’s rho=0.39 and 0.57), but not with the bittering hop component iso-alpha acids (Spearman’s rho=0.16 and −0.07). This illustrates how brewers can independently modify hop aroma and bitterness by selecting hop varieties and dosage time. If hops are added early in the boiling phase, chemical conversions increase bitterness while aromas evaporate, conversely, late addition of hops preserves aroma but limits bitterness 51 . Similarly, hop-derived iso-alpha acids show a strong anti-correlation with lactic acid and acetic acid, likely reflecting growth inhibition of lactic acid and acetic acid bacteria, or the consequent use of fewer hops in sour beer styles, such as West Flanders ales and Fruit beers, that rely on these bacteria for their distinct flavors 52 . Finally, yeast-derived esters (ethyl acetate, ethyl decanoate, ethyl hexanoate, ethyl octanoate) and alcohols (ethanol, isoamyl alcohol, isobutanol, and glycerol), correlate with Spearman coefficients above 0.5, suggesting that these secondary metabolites are correlated with the yeast genetic background and/or fermentation parameters and may be difficult to influence individually, although the choice of yeast strain may offer some control 53 .

Spearman rank correlations are shown. Descriptors are grouped according to their origin (malt (blue), hops (green), yeast (red), wild flora (yellow), Others (black)), and sensory aspect (aroma, taste, palate, and overall appreciation). Please note that for the chemical compounds, for the sake of clarity, only a subset of the total number of measured compounds is shown, with an emphasis on the key compounds for each source. For more details, see the main text and Methods section. Chemical data can be found in Supplementary Data 1 , correlations between all chemical compounds are depicted in Supplementary Fig. S2 and correlation values can be found in Supplementary Data 2 . See Supplementary Data 4 for sensory panel assessments and Supplementary Data 5 for correlation values between all sensory descriptors.

Interestingly, different beer styles show distinct patterns for some flavor compounds (Supplementary Fig. S3 ). These observations agree with expectations for key beer styles, and serve as a control for our measurements. For instance, Stouts generally show high values for color (darker), while hoppy beers contain elevated levels of iso-alpha acids, compounds associated with bitter hop taste. Acetic and lactic acid are not prevalent in most beers, with notable exceptions such as Kriek, Lambic, Faro, West Flanders ales and Flanders Old Brown, which use acid-producing bacteria ( Lactobacillus and Pediococcus ) or unconventional yeast ( Brettanomyces ) 54 , 55 . Glycerol, ethanol and esters show similar distributions across all beer styles, reflecting their common origin as products of yeast metabolism during fermentation 45 , 53 . Finally, low/no-alcohol beers contain low concentrations of glycerol and esters. This is in line with the production process for most of the low/no-alcohol beers in our dataset, which are produced through limiting fermentation or by stripping away alcohol via evaporation or dialysis, with both methods having the unintended side-effect of reducing the amount of flavor compounds in the final beer 56 , 57 .

Besides expected associations, our data also reveals less trivial associations between beer styles and specific parameters. For example, geraniol and citronellol, two monoterpenoids responsible for citrus, floral and rose flavors and characteristic of Citra hops, are found in relatively high amounts in Christmas, Saison, and Brett/co-fermented beers, where they may originate from terpenoid-rich spices such as coriander seeds instead of hops 58 .

Tasting panel assessments reveal sensorial relationships in beer

To assess the sensory profile of each beer, a trained tasting panel evaluated each of the 250 beers for 50 sensory attributes, including different hop, malt and yeast flavors, off-flavors and spices. Panelists used a tasting sheet (Supplementary Data 3 ) to score the different attributes. Panel consistency was evaluated by repeating 12 samples across different sessions and performing ANOVA. In 95% of cases no significant difference was found across sessions ( p > 0.05), indicating good panel consistency (Supplementary Table S2 ).

Aroma and taste perception reported by the trained panel are often linked (Fig. 1 , bottom left panel and Supplementary Data 4 and 5 ), with high correlations between hops aroma and taste (Spearman’s rho=0.83). Bitter taste was found to correlate with hop aroma and taste in general (Spearman’s rho=0.80 and 0.69), and particularly with “grassy” noble hops (Spearman’s rho=0.75). Barnyard flavor, most often associated with sour beers, is identified together with stale hops (Spearman’s rho=0.97) that are used in these beers. Lactic and acetic acid, which often co-occur, are correlated (Spearman’s rho=0.66). Interestingly, sweetness and bitterness are anti-correlated (Spearman’s rho = −0.48), confirming the hypothesis that they mask each other 59 , 60 . Beer body is highly correlated with alcohol (Spearman’s rho = 0.79), and overall appreciation is found to correlate with multiple aspects that describe beer mouthfeel (alcohol, carbonation; Spearman’s rho= 0.32, 0.39), as well as with hop and ester aroma intensity (Spearman’s rho=0.39 and 0.35).

Similar to the chemical analyses, sensorial analyses confirmed typical features of specific beer styles (Supplementary Fig. S4 ). For example, sour beers (Faro, Flanders Old Brown, Fruit beer, Kriek, Lambic, West Flanders ale) were rated acidic, with flavors of both acetic and lactic acid. Hoppy beers were found to be bitter and showed hop-associated aromas like citrus and tropical fruit. Malt taste is most detected among scotch, stout/porters, and strong ales, while low/no-alcohol beers, which often have a reputation for being ‘worty’ (reminiscent of unfermented, sweet malt extract) appear in the middle. Unsurprisingly, hop aromas are most strongly detected among hoppy beers. Like its chemical counterpart (Supplementary Fig. S3 ), acidity shows a right-skewed distribution, with the most acidic beers being Krieks, Lambics, and West Flanders ales.

Tasting panel assessments of specific flavors correlate with chemical composition

We find that the concentrations of several chemical compounds strongly correlate with specific aroma or taste, as evaluated by the tasting panel (Fig. 2 , Supplementary Fig. S5 , Supplementary Data 6 ). In some cases, these correlations confirm expectations and serve as a useful control for data quality. For example, iso-alpha acids, the bittering compounds in hops, strongly correlate with bitterness (Spearman’s rho=0.68), while ethanol and glycerol correlate with tasters’ perceptions of alcohol and body, the mouthfeel sensation of fullness (Spearman’s rho=0.82/0.62 and 0.72/0.57 respectively) and darker color from roasted malts is a good indication of malt perception (Spearman’s rho=0.54).

Heatmap colors indicate Spearman’s Rho. Axes are organized according to sensory categories (aroma, taste, mouthfeel, overall), chemical categories and chemical sources in beer (malt (blue), hops (green), yeast (red), wild flora (yellow), Others (black)). See Supplementary Data 6 for all correlation values.

Interestingly, for some relationships between chemical compounds and perceived flavor, correlations are weaker than expected. For example, the rose-smelling phenethyl acetate only weakly correlates with floral aroma. This hints at more complex relationships and interactions between compounds and suggests a need for a more complex model than simple correlations. Lastly, we uncovered unexpected correlations. For instance, the esters ethyl decanoate and ethyl octanoate appear to correlate slightly with hop perception and bitterness, possibly due to their fruity flavor. Iron is anti-correlated with hop aromas and bitterness, most likely because it is also anti-correlated with iso-alpha acids. This could be a sign of metal chelation of hop acids 61 , given that our analyses measure unbound hop acids and total iron content, or could result from the higher iron content in dark and Fruit beers, which typically have less hoppy and bitter flavors 62 .

Public consumer reviews complement expert panel data

To complement and expand the sensory data of our trained tasting panel, we collected 180,000 reviews of our 250 beers from the online consumer review platform RateBeer. This provided numerical scores for beer appearance, aroma, taste, palate, overall quality as well as the average overall score.

Public datasets are known to suffer from biases, such as price, cult status and psychological conformity towards previous ratings of a product. For example, prices correlate with appreciation scores for these online consumer reviews (rho=0.49, Supplementary Fig. S6 ), but not for our trained tasting panel (rho=0.19). This suggests that prices affect consumer appreciation, which has been reported in wine 63 , while blind tastings are unaffected. Moreover, we observe that some beer styles, like lagers and non-alcoholic beers, generally receive lower scores, reflecting that online reviewers are mostly beer aficionados with a preference for specialty beers over lager beers. In general, we find a modest correlation between our trained panel’s overall appreciation score and the online consumer appreciation scores (Fig. 3 , rho=0.29). Apart from the aforementioned biases in the online datasets, serving temperature, sample freshness and surroundings, which are all tightly controlled during the tasting panel sessions, can vary tremendously across online consumers and can further contribute to (among others, appreciation) differences between the two categories of tasters. Importantly, in contrast to the overall appreciation scores, for many sensory aspects the results from the professional panel correlated well with results obtained from RateBeer reviews. Correlations were highest for features that are relatively easy to recognize even for untrained tasters, like bitterness, sweetness, alcohol and malt aroma (Fig. 3 and below).

RateBeer text mining results can be found in Supplementary Data 7 . Rho values shown are Spearman correlation values, with asterisks indicating significant correlations ( p < 0.05, two-sided). All p values were smaller than 0.001, except for Esters aroma (0.0553), Esters taste (0.3275), Esters aroma—banana (0.0019), Coriander (0.0508) and Diacetyl (0.0134).

Besides collecting consumer appreciation from these online reviews, we developed automated text analysis tools to gather additional data from review texts (Supplementary Data 7 ). Processing review texts on the RateBeer database yielded comparable results to the scores given by the trained panel for many common sensory aspects, including acidity, bitterness, sweetness, alcohol, malt, and hop tastes (Fig. 3 ). This is in line with what would be expected, since these attributes require less training for accurate assessment and are less influenced by environmental factors such as temperature, serving glass and odors in the environment. Consumer reviews also correlate well with our trained panel for 4-vinyl guaiacol, a compound associated with a very characteristic aroma. By contrast, correlations for more specific aromas like ester, coriander or diacetyl are underrepresented in the online reviews, underscoring the importance of using a trained tasting panel and standardized tasting sheets with explicit factors to be scored for evaluating specific aspects of a beer. Taken together, our results suggest that public reviews are trustworthy for some, but not all, flavor features and can complement or substitute taste panel data for these sensory aspects.

Models can predict beer sensory profiles from chemical data

The rich datasets of chemical analyses, tasting panel assessments and public reviews gathered in the first part of this study provided us with a unique opportunity to develop predictive models that link chemical data to sensorial features. Given the complexity of beer flavor, basic statistical tools such as correlations or linear regression may not always be the most suitable for making accurate predictions. Instead, we applied different machine learning models that can model both simple linear and complex interactive relationships. Specifically, we constructed a set of regression models to predict (a) trained panel scores for beer flavor and quality and (b) public reviews’ appreciation scores from beer chemical profiles. We trained and tested 10 different models (Methods), 3 linear regression-based models (simple linear regression with first-order interactions (LR), lasso regression with first-order interactions (Lasso), partial least squares regressor (PLSR)), 5 decision tree models (AdaBoost regressor (ABR), extra trees (ET), gradient boosting regressor (GBR), random forest (RF) and XGBoost regressor (XGBR)), 1 support vector regression (SVR), and 1 artificial neural network (ANN) model.

To compare the performance of our machine learning models, the dataset was randomly split into a training and test set, stratified by beer style. After a model was trained on data in the training set, its performance was evaluated on its ability to predict the test dataset obtained from multi-output models (based on the coefficient of determination, see Methods). Additionally, individual-attribute models were ranked per descriptor and the average rank was calculated, as proposed by Korneva et al. 64 . Importantly, both ways of evaluating the models’ performance agreed in general. Performance of the different models varied (Table 1 ). It should be noted that all models perform better at predicting RateBeer results than results from our trained tasting panel. One reason could be that sensory data is inherently variable, and this variability is averaged out with the large number of public reviews from RateBeer. Additionally, all tree-based models perform better at predicting taste than aroma. Linear models (LR) performed particularly poorly, with negative R 2 values, due to severe overfitting (training set R 2 = 1). Overfitting is a common issue in linear models with many parameters and limited samples, especially with interaction terms further amplifying the number of parameters. L1 regularization (Lasso) successfully overcomes this overfitting, out-competing multiple tree-based models on the RateBeer dataset. Similarly, the dimensionality reduction of PLSR avoids overfitting and improves performance, to some extent. Still, tree-based models (ABR, ET, GBR, RF and XGBR) show the best performance, out-competing the linear models (LR, Lasso, PLSR) commonly used in sensory science 65 .

GBR models showed the best overall performance in predicting sensory responses from chemical information, with R 2 values up to 0.75 depending on the predicted sensory feature (Supplementary Table S4 ). The GBR models predict consumer appreciation (RateBeer) better than our trained panel’s appreciation (R 2 value of 0.67 compared to R 2 value of 0.09) (Supplementary Table S3 and Supplementary Table S4 ). ANN models showed intermediate performance, likely because neural networks typically perform best with larger datasets 66 . The SVR shows intermediate performance, mostly due to the weak predictions of specific attributes that lower the overall performance (Supplementary Table S4 ).

Model dissection identifies specific, unexpected compounds as drivers of consumer appreciation

Next, we leveraged our models to infer important contributors to sensory perception and consumer appreciation. Consumer preference is a crucial sensory aspects, because a product that shows low consumer appreciation scores often does not succeed commercially 25 . Additionally, the requirement for a large number of representative evaluators makes consumer trials one of the more costly and time-consuming aspects of product development. Hence, a model for predicting chemical drivers of overall appreciation would be a welcome addition to the available toolbox for food development and optimization.

Since GBR models on our RateBeer dataset showed the best overall performance, we focused on these models. Specifically, we used two approaches to identify important contributors. First, rankings of the most important predictors for each sensorial trait in the GBR models were obtained based on impurity-based feature importance (mean decrease in impurity). High-ranked parameters were hypothesized to be either the true causal chemical properties underlying the trait, to correlate with the actual causal properties, or to take part in sensory interactions affecting the trait 67 (Fig. 4A ). In a second approach, we used SHAP 68 to determine which parameters contributed most to the model for making predictions of consumer appreciation (Fig. 4B ). SHAP calculates parameter contributions to model predictions on a per-sample basis, which can be aggregated into an importance score.

A The impurity-based feature importance (mean deviance in impurity, MDI) calculated from the Gradient Boosting Regression (GBR) model predicting RateBeer appreciation scores. The top 15 highest ranked chemical properties are shown. B SHAP summary plot for the top 15 parameters contributing to our GBR model. Each point on the graph represents a sample from our dataset. The color represents the concentration of that parameter, with bluer colors representing low values and redder colors representing higher values. Greater absolute values on the horizontal axis indicate a higher impact of the parameter on the prediction of the model. C Spearman correlations between the 15 most important chemical properties and consumer overall appreciation. Numbers indicate the Spearman Rho correlation coefficient, and the rank of this correlation compared to all other correlations. The top 15 important compounds were determined using SHAP (panel B).

Both approaches identified ethyl acetate as the most predictive parameter for beer appreciation (Fig. 4 ). Ethyl acetate is the most abundant ester in beer with a typical ‘fruity’, ‘solvent’ and ‘alcoholic’ flavor, but is often considered less important than other esters like isoamyl acetate. The second most important parameter identified by SHAP is ethanol, the most abundant beer compound after water. Apart from directly contributing to beer flavor and mouthfeel, ethanol drastically influences the physical properties of beer, dictating how easily volatile compounds escape the beer matrix to contribute to beer aroma 69 . Importantly, it should also be noted that the importance of ethanol for appreciation is likely inflated by the very low appreciation scores of non-alcoholic beers (Supplementary Fig. S4 ). Despite not often being considered a driver of beer appreciation, protein level also ranks highly in both approaches, possibly due to its effect on mouthfeel and body 70 . Lactic acid, which contributes to the tart taste of sour beers, is the fourth most important parameter identified by SHAP, possibly due to the generally high appreciation of sour beers in our dataset.

Interestingly, some of the most important predictive parameters for our model are not well-established as beer flavors or are even commonly regarded as being negative for beer quality. For example, our models identify methanethiol and ethyl phenyl acetate, an ester commonly linked to beer staling 71 , as a key factor contributing to beer appreciation. Although there is no doubt that high concentrations of these compounds are considered unpleasant, the positive effects of modest concentrations are not yet known 72 , 73 .

To compare our approach to conventional statistics, we evaluated how well the 15 most important SHAP-derived parameters correlate with consumer appreciation (Fig. 4C ). Interestingly, only 6 of the properties derived by SHAP rank amongst the top 15 most correlated parameters. For some chemical compounds, the correlations are so low that they would have likely been considered unimportant. For example, lactic acid, the fourth most important parameter, shows a bimodal distribution for appreciation, with sour beers forming a separate cluster, that is missed entirely by the Spearman correlation. Additionally, the correlation plots reveal outliers, emphasizing the need for robust analysis tools. Together, this highlights the need for alternative models, like the Gradient Boosting model, that better grasp the complexity of (beer) flavor.

Finally, to observe the relationships between these chemical properties and their predicted targets, partial dependence plots were constructed for the six most important predictors of consumer appreciation 74 , 75 , 76 (Supplementary Fig. S7 ). One-way partial dependence plots show how a change in concentration affects the predicted appreciation. These plots reveal an important limitation of our models: appreciation predictions remain constant at ever-increasing concentrations. This implies that once a threshold concentration is reached, further increasing the concentration does not affect appreciation. This is false, as it is well-documented that certain compounds become unpleasant at high concentrations, including ethyl acetate (‘nail polish’) 77 and methanethiol (‘sulfury’ and ‘rotten cabbage’) 78 . The inability of our models to grasp that flavor compounds have optimal levels, above which they become negative, is a consequence of working with commercial beer brands where (off-)flavors are rarely too high to negatively impact the product. The two-way partial dependence plots show how changing the concentration of two compounds influences predicted appreciation, visualizing their interactions (Supplementary Fig. S7 ). In our case, the top 5 parameters are dominated by additive or synergistic interactions, with high concentrations for both compounds resulting in the highest predicted appreciation.

To assess the robustness of our best-performing models and model predictions, we performed 100 iterations of the GBR, RF and ET models. In general, all iterations of the models yielded similar performance (Supplementary Fig. S8 ). Moreover, the main predictors (including the top predictors ethanol and ethyl acetate) remained virtually the same, especially for GBR and RF. For the iterations of the ET model, we did observe more variation in the top predictors, which is likely a consequence of the model’s inherent random architecture in combination with co-correlations between certain predictors. However, even in this case, several of the top predictors (ethanol and ethyl acetate) remain unchanged, although their rank in importance changes (Supplementary Fig. S8 ).

Next, we investigated if a combination of RateBeer and trained panel data into one consolidated dataset would lead to stronger models, under the hypothesis that such a model would suffer less from bias in the datasets. A GBR model was trained to predict appreciation on the combined dataset. This model underperformed compared to the RateBeer model, both in the native case and when including a dataset identifier (R 2 = 0.67, 0.26 and 0.42 respectively). For the latter, the dataset identifier is the most important feature (Supplementary Fig. S9 ), while most of the feature importance remains unchanged, with ethyl acetate and ethanol ranking highest, like in the original model trained only on RateBeer data. It seems that the large variation in the panel dataset introduces noise, weakening the models’ performances and reliability. In addition, it seems reasonable to assume that both datasets are fundamentally different, with the panel dataset obtained by blind tastings by a trained professional panel.

Lastly, we evaluated whether beer style identifiers would further enhance the model’s performance. A GBR model was trained with parameters that explicitly encoded the styles of the samples. This did not improve model performance (R2 = 0.66 with style information vs R2 = 0.67). The most important chemical features are consistent with the model trained without style information (eg. ethanol and ethyl acetate), and with the exception of the most preferred (strong ale) and least preferred (low/no-alcohol) styles, none of the styles were among the most important features (Supplementary Fig. S9 , Supplementary Table S5 and S6 ). This is likely due to a combination of style-specific chemical signatures, such as iso-alpha acids and lactic acid, that implicitly convey style information to the original models, as well as the low number of samples belonging to some styles, making it difficult for the model to learn style-specific patterns. Moreover, beer styles are not rigorously defined, with some styles overlapping in features and some beers being misattributed to a specific style, all of which leads to more noise in models that use style parameters.

Model validation

To test if our predictive models give insight into beer appreciation, we set up experiments aimed at improving existing commercial beers. We specifically selected overall appreciation as the trait to be examined because of its complexity and commercial relevance. Beer flavor comprises a complex bouquet rather than single aromas and tastes 53 . Hence, adding a single compound to the extent that a difference is noticeable may lead to an unbalanced, artificial flavor. Therefore, we evaluated the effect of combinations of compounds. Because Blond beers represent the most extensive style in our dataset, we selected a beer from this style as the starting material for these experiments (Beer 64 in Supplementary Data 1 ).

In the first set of experiments, we adjusted the concentrations of compounds that made up the most important predictors of overall appreciation (ethyl acetate, ethanol, lactic acid, ethyl phenyl acetate) together with correlated compounds (ethyl hexanoate, isoamyl acetate, glycerol), bringing them up to 95 th percentile ethanol-normalized concentrations (Methods) within the Blond group (‘Spiked’ concentration in Fig. 5A ). Compared to controls, the spiked beers were found to have significantly improved overall appreciation among trained panelists, with panelist noting increased intensity of ester flavors, sweetness, alcohol, and body fullness (Fig. 5B ). To disentangle the contribution of ethanol to these results, a second experiment was performed without the addition of ethanol. This resulted in a similar outcome, including increased perception of alcohol and overall appreciation.

Adding the top chemical compounds, identified as best predictors of appreciation by our model, into poorly appreciated beers results in increased appreciation from our trained panel. Results of sensory tests between base beers and those spiked with compounds identified as the best predictors by the model. A Blond and Non/Low-alcohol (0.0% ABV) base beers were brought up to 95th-percentile ethanol-normalized concentrations within each style. B For each sensory attribute, tasters indicated the more intense sample and selected the sample they preferred. The numbers above the bars correspond to the p values that indicate significant changes in perceived flavor (two-sided binomial test: alpha 0.05, n = 20 or 13).

In a last experiment, we tested whether using the model’s predictions can boost the appreciation of a non-alcoholic beer (beer 223 in Supplementary Data 1 ). Again, the addition of a mixture of predicted compounds (omitting ethanol, in this case) resulted in a significant increase in appreciation, body, ester flavor and sweetness.

Predicting flavor and consumer appreciation from chemical composition is one of the ultimate goals of sensory science. A reliable, systematic and unbiased way to link chemical profiles to flavor and food appreciation would be a significant asset to the food and beverage industry. Such tools would substantially aid in quality control and recipe development, offer an efficient and cost-effective alternative to pilot studies and consumer trials and would ultimately allow food manufacturers to produce superior, tailor-made products that better meet the demands of specific consumer groups more efficiently.

A limited set of studies have previously tried, to varying degrees of success, to predict beer flavor and beer popularity based on (a limited set of) chemical compounds and flavors 79 , 80 . Current sensitive, high-throughput technologies allow measuring an unprecedented number of chemical compounds and properties in a large set of samples, yielding a dataset that can train models that help close the gaps between chemistry and flavor, even for a complex natural product like beer. To our knowledge, no previous research gathered data at this scale (250 samples, 226 chemical parameters, 50 sensory attributes and 5 consumer scores) to disentangle and validate the chemical aspects driving beer preference using various machine-learning techniques. We find that modern machine learning models outperform conventional statistical tools, such as correlations and linear models, and can successfully predict flavor appreciation from chemical composition. This could be attributed to the natural incorporation of interactions and non-linear or discontinuous effects in machine learning models, which are not easily grasped by the linear model architecture. While linear models and partial least squares regression represent the most widespread statistical approaches in sensory science, in part because they allow interpretation 65 , 81 , 82 , modern machine learning methods allow for building better predictive models while preserving the possibility to dissect and exploit the underlying patterns. Of the 10 different models we trained, tree-based models, such as our best performing GBR, showed the best overall performance in predicting sensory responses from chemical information, outcompeting artificial neural networks. This agrees with previous reports for models trained on tabular data 83 . Our results are in line with the findings of Colantonio et al. who also identified the gradient boosting architecture as performing best at predicting appreciation and flavor (of tomatoes and blueberries, in their specific study) 26 . Importantly, besides our larger experimental scale, we were able to directly confirm our models’ predictions in vivo.

Our study confirms that flavor compound concentration does not always correlate with perception, suggesting complex interactions that are often missed by more conventional statistics and simple models. Specifically, we find that tree-based algorithms may perform best in developing models that link complex food chemistry with aroma. Furthermore, we show that massive datasets of untrained consumer reviews provide a valuable source of data, that can complement or even replace trained tasting panels, especially for appreciation and basic flavors, such as sweetness and bitterness. This holds despite biases that are known to occur in such datasets, such as price or conformity bias. Moreover, GBR models predict taste better than aroma. This is likely because taste (e.g. bitterness) often directly relates to the corresponding chemical measurements (e.g., iso-alpha acids), whereas such a link is less clear for aromas, which often result from the interplay between multiple volatile compounds. We also find that our models are best at predicting acidity and alcohol, likely because there is a direct relation between the measured chemical compounds (acids and ethanol) and the corresponding perceived sensorial attribute (acidity and alcohol), and because even untrained consumers are generally able to recognize these flavors and aromas.

The predictions of our final models, trained on review data, hold even for blind tastings with small groups of trained tasters, as demonstrated by our ability to validate specific compounds as drivers of beer flavor and appreciation. Since adding a single compound to the extent of a noticeable difference may result in an unbalanced flavor profile, we specifically tested our identified key drivers as a combination of compounds. While this approach does not allow us to validate if a particular single compound would affect flavor and/or appreciation, our experiments do show that this combination of compounds increases consumer appreciation.

It is important to stress that, while it represents an important step forward, our approach still has several major limitations. A key weakness of the GBR model architecture is that amongst co-correlating variables, the largest main effect is consistently preferred for model building. As a result, co-correlating variables often have artificially low importance scores, both for impurity and SHAP-based methods, like we observed in the comparison to the more randomized Extra Trees models. This implies that chemicals identified as key drivers of a specific sensory feature by GBR might not be the true causative compounds, but rather co-correlate with the actual causative chemical. For example, the high importance of ethyl acetate could be (partially) attributed to the total ester content, ethanol or ethyl hexanoate (rho=0.77, rho=0.72 and rho=0.68), while ethyl phenylacetate could hide the importance of prenyl isobutyrate and ethyl benzoate (rho=0.77 and rho=0.76). Expanding our GBR model to include beer style as a parameter did not yield additional power or insight. This is likely due to style-specific chemical signatures, such as iso-alpha acids and lactic acid, that implicitly convey style information to the original model, as well as the smaller sample size per style, limiting the power to uncover style-specific patterns. This can be partly attributed to the curse of dimensionality, where the high number of parameters results in the models mainly incorporating single parameter effects, rather than complex interactions such as style-dependent effects 67 . A larger number of samples may overcome some of these limitations and offer more insight into style-specific effects. On the other hand, beer style is not a rigid scientific classification, and beers within one style often differ a lot, which further complicates the analysis of style as a model factor.

Our study is limited to beers from Belgian breweries. Although these beers cover a large portion of the beer styles available globally, some beer styles and consumer patterns may be missing, while other features might be overrepresented. For example, many Belgian ales exhibit yeast-driven flavor profiles, which is reflected in the chemical drivers of appreciation discovered by this study. In future work, expanding the scope to include diverse markets and beer styles could lead to the identification of even more drivers of appreciation and better models for special niche products that were not present in our beer set.

In addition to inherent limitations of GBR models, there are also some limitations associated with studying food aroma. Even if our chemical analyses measured most of the known aroma compounds, the total number of flavor compounds in complex foods like beer is still larger than the subset we were able to measure in this study. For example, hop-derived thiols, that influence flavor at very low concentrations, are notoriously difficult to measure in a high-throughput experiment. Moreover, consumer perception remains subjective and prone to biases that are difficult to avoid. It is also important to stress that the models are still immature and that more extensive datasets will be crucial for developing more complete models in the future. Besides more samples and parameters, our dataset does not include any demographic information about the tasters. Including such data could lead to better models that grasp external factors like age and culture. Another limitation is that our set of beers consists of high-quality end-products and lacks beers that are unfit for sale, which limits the current model in accurately predicting products that are appreciated very badly. Finally, while models could be readily applied in quality control, their use in sensory science and product development is restrained by their inability to discern causal relationships. Given that the models cannot distinguish compounds that genuinely drive consumer perception from those that merely correlate, validation experiments are essential to identify true causative compounds.

Despite the inherent limitations, dissection of our models enabled us to pinpoint specific molecules as potential drivers of beer aroma and consumer appreciation, including compounds that were unexpected and would not have been identified using standard approaches. Important drivers of beer appreciation uncovered by our models include protein levels, ethyl acetate, ethyl phenyl acetate and lactic acid. Currently, many brewers already use lactic acid to acidify their brewing water and ensure optimal pH for enzymatic activity during the mashing process. Our results suggest that adding lactic acid can also improve beer appreciation, although its individual effect remains to be tested. Interestingly, ethanol appears to be unnecessary to improve beer appreciation, both for blond beer and alcohol-free beer. Given the growing consumer interest in alcohol-free beer, with a predicted annual market growth of >7% 84 , it is relevant for brewers to know what compounds can further increase consumer appreciation of these beers. Hence, our model may readily provide avenues to further improve the flavor and consumer appreciation of both alcoholic and non-alcoholic beers, which is generally considered one of the key challenges for future beer production.

Whereas we see a direct implementation of our results for the development of superior alcohol-free beverages and other food products, our study can also serve as a stepping stone for the development of novel alcohol-containing beverages. We want to echo the growing body of scientific evidence for the negative effects of alcohol consumption, both on the individual level by the mutagenic, teratogenic and carcinogenic effects of ethanol 85 , 86 , as well as the burden on society caused by alcohol abuse and addiction. We encourage the use of our results for the production of healthier, tastier products, including novel and improved beverages with lower alcohol contents. Furthermore, we strongly discourage the use of these technologies to improve the appreciation or addictive properties of harmful substances.

The present work demonstrates that despite some important remaining hurdles, combining the latest developments in chemical analyses, sensory analysis and modern machine learning methods offers exciting avenues for food chemistry and engineering. Soon, these tools may provide solutions in quality control and recipe development, as well as new approaches to sensory science and flavor research.

Beer selection

250 commercial Belgian beers were selected to cover the broad diversity of beer styles and corresponding diversity in chemical composition and aroma. See Supplementary Fig. S1 .

Chemical dataset

Sample preparation.

Beers within their expiration date were purchased from commercial retailers. Samples were prepared in biological duplicates at room temperature, unless explicitly stated otherwise. Bottle pressure was measured with a manual pressure device (Steinfurth Mess-Systeme GmbH) and used to calculate CO 2 concentration. The beer was poured through two filter papers (Macherey-Nagel, 500713032 MN 713 ¼) to remove carbon dioxide and prevent spontaneous foaming. Samples were then prepared for measurements by targeted Headspace-Gas Chromatography-Flame Ionization Detector/Flame Photometric Detector (HS-GC-FID/FPD), Headspace-Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS), colorimetric analysis, enzymatic analysis, Near-Infrared (NIR) analysis, as described in the sections below. The mean values of biological duplicates are reported for each compound.

HS-GC-FID/FPD

HS-GC-FID/FPD (Shimadzu GC 2010 Plus) was used to measure higher alcohols, acetaldehyde, esters, 4-vinyl guaicol, and sulfur compounds. Each measurement comprised 5 ml of sample pipetted into a 20 ml glass vial containing 1.75 g NaCl (VWR, 27810.295). 100 µl of 2-heptanol (Sigma-Aldrich, H3003) (internal standard) solution in ethanol (Fisher Chemical, E/0650DF/C17) was added for a final concentration of 2.44 mg/L. Samples were flushed with nitrogen for 10 s, sealed with a silicone septum, stored at −80 °C and analyzed in batches of 20.

The GC was equipped with a DB-WAXetr column (length, 30 m; internal diameter, 0.32 mm; layer thickness, 0.50 µm; Agilent Technologies, Santa Clara, CA, USA) to the FID and an HP-5 column (length, 30 m; internal diameter, 0.25 mm; layer thickness, 0.25 µm; Agilent Technologies, Santa Clara, CA, USA) to the FPD. N 2 was used as the carrier gas. Samples were incubated for 20 min at 70 °C in the headspace autosampler (Flow rate, 35 cm/s; Injection volume, 1000 µL; Injection mode, split; Combi PAL autosampler, CTC analytics, Switzerland). The injector, FID and FPD temperatures were kept at 250 °C. The GC oven temperature was first held at 50 °C for 5 min and then allowed to rise to 80 °C at a rate of 5 °C/min, followed by a second ramp of 4 °C/min until 200 °C kept for 3 min and a final ramp of (4 °C/min) until 230 °C for 1 min. Results were analyzed with the GCSolution software version 2.4 (Shimadzu, Kyoto, Japan). The GC was calibrated with a 5% EtOH solution (VWR International) containing the volatiles under study (Supplementary Table S7 ).

HS-SPME-GC-MS

HS-SPME-GC-MS (Shimadzu GCMS-QP-2010 Ultra) was used to measure additional volatile compounds, mainly comprising terpenoids and esters. Samples were analyzed by HS-SPME using a triphase DVB/Carboxen/PDMS 50/30 μm SPME fiber (Supelco Co., Bellefonte, PA, USA) followed by gas chromatography (Thermo Fisher Scientific Trace 1300 series, USA) coupled to a mass spectrometer (Thermo Fisher Scientific ISQ series MS) equipped with a TriPlus RSH autosampler. 5 ml of degassed beer sample was placed in 20 ml vials containing 1.75 g NaCl (VWR, 27810.295). 5 µl internal standard mix was added, containing 2-heptanol (1 g/L) (Sigma-Aldrich, H3003), 4-fluorobenzaldehyde (1 g/L) (Sigma-Aldrich, 128376), 2,3-hexanedione (1 g/L) (Sigma-Aldrich, 144169) and guaiacol (1 g/L) (Sigma-Aldrich, W253200) in ethanol (Fisher Chemical, E/0650DF/C17). Each sample was incubated at 60 °C in the autosampler oven with constant agitation. After 5 min equilibration, the SPME fiber was exposed to the sample headspace for 30 min. The compounds trapped on the fiber were thermally desorbed in the injection port of the chromatograph by heating the fiber for 15 min at 270 °C.

The GC-MS was equipped with a low polarity RXi-5Sil MS column (length, 20 m; internal diameter, 0.18 mm; layer thickness, 0.18 µm; Restek, Bellefonte, PA, USA). Injection was performed in splitless mode at 320 °C, a split flow of 9 ml/min, a purge flow of 5 ml/min and an open valve time of 3 min. To obtain a pulsed injection, a programmed gas flow was used whereby the helium gas flow was set at 2.7 mL/min for 0.1 min, followed by a decrease in flow of 20 ml/min to the normal 0.9 mL/min. The temperature was first held at 30 °C for 3 min and then allowed to rise to 80 °C at a rate of 7 °C/min, followed by a second ramp of 2 °C/min till 125 °C and a final ramp of 8 °C/min with a final temperature of 270 °C.

Mass acquisition range was 33 to 550 amu at a scan rate of 5 scans/s. Electron impact ionization energy was 70 eV. The interface and ion source were kept at 275 °C and 250 °C, respectively. A mix of linear n-alkanes (from C7 to C40, Supelco Co.) was injected into the GC-MS under identical conditions to serve as external retention index markers. Identification and quantification of the compounds were performed using an in-house developed R script as described in Goelen et al. and Reher et al. 87 , 88 (for package information, see Supplementary Table S8 ). Briefly, chromatograms were analyzed using AMDIS (v2.71) 89 to separate overlapping peaks and obtain pure compound spectra. The NIST MS Search software (v2.0 g) in combination with the NIST2017, FFNSC3 and Adams4 libraries were used to manually identify the empirical spectra, taking into account the expected retention time. After background subtraction and correcting for retention time shifts between samples run on different days based on alkane ladders, compound elution profiles were extracted and integrated using a file with 284 target compounds of interest, which were either recovered in our identified AMDIS list of spectra or were known to occur in beer. Compound elution profiles were estimated for every peak in every chromatogram over a time-restricted window using weighted non-negative least square analysis after which peak areas were integrated 87 , 88 . Batch effect correction was performed by normalizing against the most stable internal standard compound, 4-fluorobenzaldehyde. Out of all 284 target compounds that were analyzed, 167 were visually judged to have reliable elution profiles and were used for final analysis.

Discrete photometric and enzymatic analysis

Discrete photometric and enzymatic analysis (Thermo Scientific TM Gallery TM Plus Beermaster Discrete Analyzer) was used to measure acetic acid, ammonia, beta-glucan, iso-alpha acids, color, sugars, glycerol, iron, pH, protein, and sulfite. 2 ml of sample volume was used for the analyses. Information regarding the reagents and standard solutions used for analyses and calibrations is included in Supplementary Table S7 and Supplementary Table S9 .

NIR analyses

NIR analysis (Anton Paar Alcolyzer Beer ME System) was used to measure ethanol. Measurements comprised 50 ml of sample, and a 10% EtOH solution was used for calibration.

Correlation calculations

Pairwise Spearman Rank correlations were calculated between all chemical properties.

Sensory dataset

Trained panel.

Our trained tasting panel consisted of volunteers who gave prior verbal informed consent. All compounds used for the validation experiment were of food-grade quality. The tasting sessions were approved by the Social and Societal Ethics Committee of the KU Leuven (G-2022-5677-R2(MAR)). All online reviewers agreed to the Terms and Conditions of the RateBeer website.

Sensory analysis was performed according to the American Society of Brewing Chemists (ASBC) Sensory Analysis Methods 90 . 30 volunteers were screened through a series of triangle tests. The sixteen most sensitive and consistent tasters were retained as taste panel members. The resulting panel was diverse in age [22–42, mean: 29], sex [56% male] and nationality [7 different countries]. The panel developed a consensus vocabulary to describe beer aroma, taste and mouthfeel. Panelists were trained to identify and score 50 different attributes, using a 7-point scale to rate attributes’ intensity. The scoring sheet is included as Supplementary Data 3 . Sensory assessments took place between 10–12 a.m. The beers were served in black-colored glasses. Per session, between 5 and 12 beers of the same style were tasted at 12 °C to 16 °C. Two reference beers were added to each set and indicated as ‘Reference 1 & 2’, allowing panel members to calibrate their ratings. Not all panelists were present at every tasting. Scores were scaled by standard deviation and mean-centered per taster. Values are represented as z-scores and clustered by Euclidean distance. Pairwise Spearman correlations were calculated between taste and aroma sensory attributes. Panel consistency was evaluated by repeating samples on different sessions and performing ANOVA to identify differences, using the ‘stats’ package (v4.2.2) in R (for package information, see Supplementary Table S8 ).

Online reviews from a public database

The ‘scrapy’ package in Python (v3.6) (for package information, see Supplementary Table S8 ). was used to collect 232,288 online reviews (mean=922, min=6, max=5343) from RateBeer, an online beer review database. Each review entry comprised 5 numerical scores (appearance, aroma, taste, palate and overall quality) and an optional review text. The total number of reviews per reviewer was collected separately. Numerical scores were scaled and centered per rater, and mean scores were calculated per beer.

For the review texts, the language was estimated using the packages ‘langdetect’ and ‘langid’ in Python. Reviews that were classified as English by both packages were kept. Reviewers with fewer than 100 entries overall were discarded. 181,025 reviews from >6000 reviewers from >40 countries remained. Text processing was done using the ‘nltk’ package in Python. Texts were corrected for slang and misspellings; proper nouns and rare words that are relevant to the beer context were specified and kept as-is (‘Chimay’,’Lambic’, etc.). A dictionary of semantically similar sensorial terms, for example ‘floral’ and ‘flower’, was created and collapsed together into one term. Words were stemmed and lemmatized to avoid identifying words such as ‘acid’ and ‘acidity’ as separate terms. Numbers and punctuation were removed.

Sentences from up to 50 randomly chosen reviews per beer were manually categorized according to the aspect of beer they describe (appearance, aroma, taste, palate, overall quality—not to be confused with the 5 numerical scores described above) or flagged as irrelevant if they contained no useful information. If a beer contained fewer than 50 reviews, all reviews were manually classified. This labeled data set was used to train a model that classified the rest of the sentences for all beers 91 . Sentences describing taste and aroma were extracted, and term frequency–inverse document frequency (TFIDF) was implemented to calculate enrichment scores for sensorial words per beer.

The sex of the tasting subject was not considered when building our sensory database. Instead, results from different panelists were averaged, both for our trained panel (56% male, 44% female) and the RateBeer reviews (70% male, 30% female for RateBeer as a whole).

Beer price collection and processing

Beer prices were collected from the following stores: Colruyt, Delhaize, Total Wine, BeerHawk, The Belgian Beer Shop, The Belgian Shop, and Beer of Belgium. Where applicable, prices were converted to Euros and normalized per liter. Spearman correlations were calculated between these prices and mean overall appreciation scores from RateBeer and the taste panel, respectively.

Pairwise Spearman Rank correlations were calculated between all sensory properties.

Machine learning models

Predictive modeling of sensory profiles from chemical data.

Regression models were constructed to predict (a) trained panel scores for beer flavors and quality from beer chemical profiles and (b) public reviews’ appreciation scores from beer chemical profiles. Z-scores were used to represent sensory attributes in both data sets. Chemical properties with log-normal distributions (Shapiro-Wilk test, p < 0.05 ) were log-transformed. Missing chemical measurements (0.1% of all data) were replaced with mean values per attribute. Observations from 250 beers were randomly separated into a training set (70%, 175 beers) and a test set (30%, 75 beers), stratified per beer style. Chemical measurements (p = 231) were normalized based on the training set average and standard deviation. In total, three linear regression-based models: linear regression with first-order interaction terms (LR), lasso regression with first-order interaction terms (Lasso) and partial least squares regression (PLSR); five decision tree models, Adaboost regressor (ABR), Extra Trees (ET), Gradient Boosting regressor (GBR), Random Forest (RF) and XGBoost regressor (XGBR); one support vector machine model (SVR) and one artificial neural network model (ANN) were trained. The models were implemented using the ‘scikit-learn’ package (v1.2.2) and ‘xgboost’ package (v1.7.3) in Python (v3.9.16). Models were trained, and hyperparameters optimized, using five-fold cross-validated grid search with the coefficient of determination (R 2 ) as the evaluation metric. The ANN (scikit-learn’s MLPRegressor) was optimized using Bayesian Tree-Structured Parzen Estimator optimization with the ‘Optuna’ Python package (v3.2.0). Individual models were trained per attribute, and a multi-output model was trained on all attributes simultaneously.

Model dissection

GBR was found to outperform other methods, resulting in models with the highest average R 2 values in both trained panel and public review data sets. Impurity-based rankings of the most important predictors for each predicted sensorial trait were obtained using the ‘scikit-learn’ package. To observe the relationships between these chemical properties and their predicted targets, partial dependence plots (PDP) were constructed for the six most important predictors of consumer appreciation 74 , 75 .

The ‘SHAP’ package in Python (v0.41.0) was implemented to provide an alternative ranking of predictor importance and to visualize the predictors’ effects as a function of their concentration 68 .

Validation of causal chemical properties

To validate the effects of the most important model features on predicted sensory attributes, beers were spiked with the chemical compounds identified by the models and descriptive sensory analyses were carried out according to the American Society of Brewing Chemists (ASBC) protocol 90 .

Compound spiking was done 30 min before tasting. Compounds were spiked into fresh beer bottles, that were immediately resealed and inverted three times. Fresh bottles of beer were opened for the same duration, resealed, and inverted thrice, to serve as controls. Pairs of spiked samples and controls were served simultaneously, chilled and in dark glasses as outlined in the Trained panel section above. Tasters were instructed to select the glass with the higher flavor intensity for each attribute (directional difference test 92 ) and to select the glass they prefer.

The final concentration after spiking was equal to the within-style average, after normalizing by ethanol concentration. This was done to ensure balanced flavor profiles in the final spiked beer. The same methods were applied to improve a non-alcoholic beer. Compounds were the following: ethyl acetate (Merck KGaA, W241415), ethyl hexanoate (Merck KGaA, W243906), isoamyl acetate (Merck KGaA, W205508), phenethyl acetate (Merck KGaA, W285706), ethanol (96%, Colruyt), glycerol (Merck KGaA, W252506), lactic acid (Merck KGaA, 261106).

Significant differences in preference or perceived intensity were determined by performing the two-sided binomial test on each attribute.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The data that support the findings of this work are available in the Supplementary Data files and have been deposited to Zenodo under accession code 10653704 93 . The RateBeer scores data are under restricted access, they are not publicly available as they are property of RateBeer (ZX Ventures, USA). Access can be obtained from the authors upon reasonable request and with permission of RateBeer (ZX Ventures, USA). Source data are provided with this paper.

Code availability

The code for training the machine learning models, analyzing the models, and generating the figures has been deposited to Zenodo under accession code 10653704 93 .

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Acknowledgements

We thank all lab members for their discussions and thank all tasting panel members for their contributions. Special thanks go out to Dr. Karin Voordeckers for her tremendous help in proofreading and improving the manuscript. M.S. was supported by a Baillet-Latour fellowship, L.C. acknowledges financial support from KU Leuven (C16/17/006), F.A.T. was supported by a PhD fellowship from FWO (1S08821N). Research in the lab of K.J.V. is supported by KU Leuven, FWO, VIB, VLAIO and the Brewing Science Serves Health Fund. Research in the lab of T.W. is supported by FWO (G.0A51.15) and KU Leuven (C16/17/006).

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These authors contributed equally: Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni.

Authors and Affiliations

VIB—KU Leuven Center for Microbiology, Gaston Geenslaan 1, B-3001, Leuven, Belgium

Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni, Lloyd Cool, Beatriz Herrera-Malaver, Florian A. Theßeling & Kevin J. Verstrepen

CMPG Laboratory of Genetics and Genomics, KU Leuven, Gaston Geenslaan 1, B-3001, Leuven, Belgium

Leuven Institute for Beer Research (LIBR), Gaston Geenslaan 1, B-3001, Leuven, Belgium

Laboratory of Socioecology and Social Evolution, KU Leuven, Naamsestraat 59, B-3000, Leuven, Belgium

Lloyd Cool, Christophe Vanderaa & Tom Wenseleers

VIB Bioinformatics Core, VIB, Rijvisschestraat 120, B-9052, Ghent, Belgium

Łukasz Kreft & Alexander Botzki

AB InBev SA/NV, Brouwerijplein 1, B-3000, Leuven, Belgium

Philippe Malcorps & Luk Daenen

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Contributions

S.P., M.S. and K.J.V. conceived the experiments. S.P., M.S. and K.J.V. designed the experiments. S.P., M.S., M.R., B.H. and F.A.T. performed the experiments. S.P., M.S., L.C., C.V., L.K., A.B., P.M., L.D., T.W. and K.J.V. contributed analysis ideas. S.P., M.S., L.C., C.V., T.W. and K.J.V. analyzed the data. All authors contributed to writing the manuscript.

Corresponding author

Correspondence to Kevin J. Verstrepen .

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K.J.V. is affiliated with bar.on. The other authors declare no competing interests.

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Schreurs, M., Piampongsant, S., Roncoroni, M. et al. Predicting and improving complex beer flavor through machine learning. Nat Commun 15 , 2368 (2024). https://doi.org/10.1038/s41467-024-46346-0

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Each review entry comprised 5 numerical scores (appearance, aroma, taste, palate and overall quality) and an optional review text. The total number of reviews per reviewer was collected separately.

Making sense of research: A guide for critiquing a paper

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Introduction

Thesis Statement

Article Summary

Your Analysis

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How to write a critique

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Introduction

Critical evaluation

Reference list

Further information

Global links and information

Acknowledgement of Traditional Owners

Introduction

Conclusion/Recommendation

Helpful Handouts

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How to Critique a Research Article

How to Critique

Title of Study/Research

Introduction

Materials and Methods

Conclusions

Final Thoughts

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Writing a Critique

Types of Critiques

Defining Critique