approach to writing research articles

How To Write A Research Paper

Step-By-Step Tutorial With Examples + FREE Template

By: Derek Jansen (MBA) | Expert Reviewer: Dr Eunice Rautenbach | March 2024

For many students, crafting a strong research paper from scratch can feel like a daunting task – and rightly so! In this post, we’ll unpack what a research paper is, what it needs to do , and how to write one – in three easy steps. 🙂 

Overview: Writing A Research Paper

What (exactly) is a research paper.

• How to write a research paper
• Stage 1 : Topic & literature search
• Stage 2 : Structure & outline
• Stage 3 : Iterative writing
• Key takeaways

Let’s start by asking the most important question, “ What is a research paper? ”.

Simply put, a research paper is a scholarly written work where the writer (that’s you!) answers a specific question (this is called a research question ) through evidence-based arguments . Evidence-based is the keyword here. In other words, a research paper is different from an essay or other writing assignments that draw from the writer’s personal opinions or experiences. With a research paper, it’s all about building your arguments based on evidence (we’ll talk more about that evidence a little later).

Now, it’s worth noting that there are many different types of research papers , including analytical papers (the type I just described), argumentative papers, and interpretative papers. Here, we’ll focus on analytical papers , as these are some of the most common – but if you’re keen to learn about other types of research papers, be sure to check out the rest of the blog .

With that basic foundation laid, let’s get down to business and look at how to write a research paper .

Overview: The 3-Stage Process

While there are, of course, many potential approaches you can take to write a research paper, there are typically three stages to the writing process. So, in this tutorial, we’ll present a straightforward three-step process that we use when working with students at Grad Coach.

These three steps are:

• Finding a research topic and reviewing the existing literature
• Developing a provisional structure and outline for your paper, and
• Writing up your initial draft and then refining it iteratively

Let’s dig into each of these.

Need a helping hand?

Step 1: Find a topic and review the literature

As we mentioned earlier, in a research paper, you, as the researcher, will try to answer a question . More specifically, that’s called a research question , and it sets the direction of your entire paper. What’s important to understand though is that you’ll need to answer that research question with the help of high-quality sources – for example, journal articles, government reports, case studies, and so on. We’ll circle back to this in a minute.

The first stage of the research process is deciding on what your research question will be and then reviewing the existing literature (in other words, past studies and papers) to see what they say about that specific research question. In some cases, your professor may provide you with a predetermined research question (or set of questions). However, in many cases, you’ll need to find your own research question within a certain topic area.

Finding a strong research question hinges on identifying a meaningful research gap – in other words, an area that’s lacking in existing research. There’s a lot to unpack here, so if you wanna learn more, check out the plain-language explainer video below.

Once you’ve figured out which question (or questions) you’ll attempt to answer in your research paper, you’ll need to do a deep dive into the existing literature – this is called a “ literature search ”. Again, there are many ways to go about this, but your most likely starting point will be Google Scholar .

If you’re new to Google Scholar, think of it as Google for the academic world. You can start by simply entering a few different keywords that are relevant to your research question and it will then present a host of articles for you to review. What you want to pay close attention to here is the number of citations for each paper – the more citations a paper has, the more credible it is (generally speaking – there are some exceptions, of course).

Ideally, what you’re looking for are well-cited papers that are highly relevant to your topic. That said, keep in mind that citations are a cumulative metric , so older papers will often have more citations than newer papers – just because they’ve been around for longer. So, don’t fixate on this metric in isolation – relevance and recency are also very important.

Beyond Google Scholar, you’ll also definitely want to check out academic databases and aggregators such as Science Direct, PubMed, JStor and so on. These will often overlap with the results that you find in Google Scholar, but they can also reveal some hidden gems – so, be sure to check them out.

Once you’ve worked your way through all the literature, you’ll want to catalogue all this information in some sort of spreadsheet so that you can easily recall who said what, when and within what context. If you’d like, we’ve got a free literature spreadsheet that helps you do exactly that.

Step 2: Develop a structure and outline

With your research question pinned down and your literature digested and catalogued, it’s time to move on to planning your actual research paper .

It might sound obvious, but it’s really important to have some sort of rough outline in place before you start writing your paper. So often, we see students eagerly rushing into the writing phase, only to land up with a disjointed research paper that rambles on in multiple

Now, the secret here is to not get caught up in the fine details . Realistically, all you need at this stage is a bullet-point list that describes (in broad strokes) what you’ll discuss and in what order. It’s also useful to remember that you’re not glued to this outline – in all likelihood, you’ll chop and change some sections once you start writing, and that’s perfectly okay. What’s important is that you have some sort of roadmap in place from the start.

At this stage you might be wondering, “ But how should I structure my research paper? ”. Well, there’s no one-size-fits-all solution here, but in general, a research paper will consist of a few relatively standardised components:

• Introduction
• Literature review
• Methodology

Let’s take a look at each of these.

First up is the introduction section . As the name suggests, the purpose of the introduction is to set the scene for your research paper. There are usually (at least) four ingredients that go into this section – these are the background to the topic, the research problem and resultant research question , and the justification or rationale. If you’re interested, the video below unpacks the introduction section in more detail. 

The next section of your research paper will typically be your literature review . Remember all that literature you worked through earlier? Well, this is where you’ll present your interpretation of all that content . You’ll do this by writing about recent trends, developments, and arguments within the literature – but more specifically, those that are relevant to your research question . The literature review can oftentimes seem a little daunting, even to seasoned researchers, so be sure to check out our extensive collection of literature review content here .

With the introduction and lit review out of the way, the next section of your paper is the research methodology . In a nutshell, the methodology section should describe to your reader what you did (beyond just reviewing the existing literature) to answer your research question. For example, what data did you collect, how did you collect that data, how did you analyse that data and so on? For each choice, you’ll also need to justify why you chose to do it that way, and what the strengths and weaknesses of your approach were.

Now, it’s worth mentioning that for some research papers, this aspect of the project may be a lot simpler . For example, you may only need to draw on secondary sources (in other words, existing data sets). In some cases, you may just be asked to draw your conclusions from the literature search itself (in other words, there may be no data analysis at all). But, if you are required to collect and analyse data, you’ll need to pay a lot of attention to the methodology section. The video below provides an example of what the methodology section might look like.

By this stage of your paper, you will have explained what your research question is, what the existing literature has to say about that question, and how you analysed additional data to try to answer your question. So, the natural next step is to present your analysis of that data . This section is usually called the “results” or “analysis” section and this is where you’ll showcase your findings.

Depending on your school’s requirements, you may need to present and interpret the data in one section – or you might split the presentation and the interpretation into two sections. In the latter case, your “results” section will just describe the data, and the “discussion” is where you’ll interpret that data and explicitly link your analysis back to your research question. If you’re not sure which approach to take, check in with your professor or take a look at past papers to see what the norms are for your programme.

Alright – once you’ve presented and discussed your results, it’s time to wrap it up . This usually takes the form of the “ conclusion ” section. In the conclusion, you’ll need to highlight the key takeaways from your study and close the loop by explicitly answering your research question. Again, the exact requirements here will vary depending on your programme (and you may not even need a conclusion section at all) – so be sure to check with your professor if you’re unsure.

Step 3: Write and refine

Finally, it’s time to get writing. All too often though, students hit a brick wall right about here… So, how do you avoid this happening to you?

Well, there’s a lot to be said when it comes to writing a research paper (or any sort of academic piece), but we’ll share three practical tips to help you get started.

First and foremost , it’s essential to approach your writing as an iterative process. In other words, you need to start with a really messy first draft and then polish it over multiple rounds of editing. Don’t waste your time trying to write a perfect research paper in one go. Instead, take the pressure off yourself by adopting an iterative approach.

Secondly , it’s important to always lean towards critical writing , rather than descriptive writing. What does this mean? Well, at the simplest level, descriptive writing focuses on the “ what ”, while critical writing digs into the “ so what ” – in other words, the implications. If you’re not familiar with these two types of writing, don’t worry! You can find a plain-language explanation here.

Last but not least, you’ll need to get your referencing right. Specifically, you’ll need to provide credible, correctly formatted citations for the statements you make. We see students making referencing mistakes all the time and it costs them dearly. The good news is that you can easily avoid this by using a simple reference manager . If you don’t have one, check out our video about Mendeley, an easy (and free) reference management tool that you can start using today.

Recap: Key Takeaways

We’ve covered a lot of ground here. To recap, the three steps to writing a high-quality research paper are:

• To choose a research question and review the literature
• To plan your paper structure and draft an outline
• To take an iterative approach to writing, focusing on critical writing and strong referencing

Remember, this is just a b ig-picture overview of the research paper development process and there’s a lot more nuance to unpack. So, be sure to grab a copy of our free research paper template to learn more about how to write a research paper.

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Article Contents

Primacy of the research question, structure of the paper, writing a research article: advice to beginners.

• Article contents
• Figures & tables
• Supplementary Data

Thomas V. Perneger, Patricia M. Hudelson, Writing a research article: advice to beginners, International Journal for Quality in Health Care , Volume 16, Issue 3, June 2004, Pages 191–192, https://doi.org/10.1093/intqhc/mzh053

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Writing research papers does not come naturally to most of us. The typical research paper is a highly codified rhetorical form [ 1 , 2 ]. Knowledge of the rules—some explicit, others implied—goes a long way toward writing a paper that will get accepted in a peer-reviewed journal.

A good research paper addresses a specific research question. The research question—or study objective or main research hypothesis—is the central organizing principle of the paper. Whatever relates to the research question belongs in the paper; the rest doesn’t. This is perhaps obvious when the paper reports on a well planned research project. However, in applied domains such as quality improvement, some papers are written based on projects that were undertaken for operational reasons, and not with the primary aim of producing new knowledge. In such cases, authors should define the main research question a posteriori and design the paper around it.

Generally, only one main research question should be addressed in a paper (secondary but related questions are allowed). If a project allows you to explore several distinct research questions, write several papers. For instance, if you measured the impact of obtaining written consent on patient satisfaction at a specialized clinic using a newly developed questionnaire, you may want to write one paper on the questionnaire development and validation, and another on the impact of the intervention. The idea is not to split results into ‘least publishable units’, a practice that is rightly decried, but rather into ‘optimally publishable units’.

What is a good research question? The key attributes are: (i) specificity; (ii) originality or novelty; and (iii) general relevance to a broad scientific community. The research question should be precise and not merely identify a general area of inquiry. It can often (but not always) be expressed in terms of a possible association between X and Y in a population Z, for example ‘we examined whether providing patients about to be discharged from the hospital with written information about their medications would improve their compliance with the treatment 1 month later’. A study does not necessarily have to break completely new ground, but it should extend previous knowledge in a useful way, or alternatively refute existing knowledge. Finally, the question should be of interest to others who work in the same scientific area. The latter requirement is more challenging for those who work in applied science than for basic scientists. While it may safely be assumed that the human genome is the same worldwide, whether the results of a local quality improvement project have wider relevance requires careful consideration and argument.

Once the research question is clearly defined, writing the paper becomes considerably easier. The paper will ask the question, then answer it. The key to successful scientific writing is getting the structure of the paper right. The basic structure of a typical research paper is the sequence of Introduction, Methods, Results, and Discussion (sometimes abbreviated as IMRAD). Each section addresses a different objective. The authors state: (i) the problem they intend to address—in other terms, the research question—in the Introduction; (ii) what they did to answer the question in the Methods section; (iii) what they observed in the Results section; and (iv) what they think the results mean in the Discussion.

In turn, each basic section addresses several topics, and may be divided into subsections (Table 1 ). In the Introduction, the authors should explain the rationale and background to the study. What is the research question, and why is it important to ask it? While it is neither necessary nor desirable to provide a full-blown review of the literature as a prelude to the study, it is helpful to situate the study within some larger field of enquiry. The research question should always be spelled out, and not merely left for the reader to guess.

Typical structure of a research paper

The Methods section should provide the readers with sufficient detail about the study methods to be able to reproduce the study if so desired. Thus, this section should be specific, concrete, technical, and fairly detailed. The study setting, the sampling strategy used, instruments, data collection methods, and analysis strategies should be described. In the case of qualitative research studies, it is also useful to tell the reader which research tradition the study utilizes and to link the choice of methodological strategies with the research goals [ 3 ].

The Results section is typically fairly straightforward and factual. All results that relate to the research question should be given in detail, including simple counts and percentages. Resist the temptation to demonstrate analytic ability and the richness of the dataset by providing numerous tables of non-essential results.

The Discussion section allows the most freedom. This is why the Discussion is the most difficult to write, and is often the weakest part of a paper. Structured Discussion sections have been proposed by some journal editors [ 4 ]. While strict adherence to such rules may not be necessary, following a plan such as that proposed in Table 1 may help the novice writer stay on track.

References should be used wisely. Key assertions should be referenced, as well as the methods and instruments used. However, unless the paper is a comprehensive review of a topic, there is no need to be exhaustive. Also, references to unpublished work, to documents in the grey literature (technical reports), or to any source that the reader will have difficulty finding or understanding should be avoided.

Having the structure of the paper in place is a good start. However, there are many details that have to be attended to while writing. An obvious recommendation is to read, and follow, the instructions to authors published by the journal (typically found on the journal’s website). Another concerns non-native writers of English: do have a native speaker edit the manuscript. A paper usually goes through several drafts before it is submitted. When revising a paper, it is useful to keep an eye out for the most common mistakes (Table 2 ). If you avoid all those, your paper should be in good shape.

Common mistakes seen in manuscripts submitted to this journal

Huth EJ . How to Write and Publish Papers in the Medical Sciences , 2nd edition. Baltimore, MD: Williams & Wilkins, 1990 .

Browner WS . Publishing and Presenting Clinical Research . Baltimore, MD: Lippincott, Williams & Wilkins, 1999 .

Devers KJ , Frankel RM. Getting qualitative research published. Educ Health 2001 ; 14 : 109 –117.

Docherty M , Smith R. The case for structuring the discussion of scientific papers. Br Med J 1999 ; 318 : 1224 –1225.

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• Published: 16 April 2021

How to approach academic writing

• Fiona Ellwood 1  

BDJ Team volume  8 ,  pages 20–21 ( 2021 ) Cite this article

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Fiona Ellwood says that writing at a higher level is an art and a craft and comes with practice and experience.

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One of the greatest challenges in academic writing is knowing where to start and what is expected. This can be particularly challenging if you have perhaps not written or been part of writing academic pieces. Those who have undertaken university level education may find this less challenging, but this is not always the case. There is certainly an art to writing at a higher level and whilst it may be a gift to some, more often, it is an art, a craft and comes with practice and experience.

The purpose of this article is to shed some light on many of the unknowns of academic writing and to take away some of the myths and untruths. Primarily the article will focus upon academic writing at university, but once this is mastered it must be noted that it is a skill that will prove invaluable in article and paper writing and is likely to help in preparation of reports and responses to consultations and so much more.

With the focus upon an academic paper, one of the first tips is to ensure the module handbook and task in hand is understood and the expectations are clear. It may be that at first there is a whole new meaning to words and an element of confusion. As noted earlier, academic writing is an art and a craft and when writing for the purpose of addressing a university module there are clearly defined components that must be met. So, whilst you have the autonomy to select the focus, there are almost always defined parameters and requirements that must be adhered to, to at least receive a satisfactory mark. 1

Before doing this, it is perhaps pertinent to discover how you best learn, how you intend to gather and store relevant information and how to establish a best way of working. One of the most useful tips is to plan and structure the paper, design an outline and a timeline. This will help you manage your time well and help you to focus.

Almost always a module will have a subject focus for example, in an evidence-based practice module, designed to help you prepare for the writing of a final dissertation. You may be faced with the following assessment task:

Report: 3,500 words (+/- 10%)1

A critical report, which provides evidence of the acquisition of the knowledge, skills and understanding required to devise, plan, implement, analyse, critically evaluate, and synthesise a small-scale piece of educational research at level 7.

So, to avoid 'writer's block' 2 when faced with such a task, it is important to break the task down. The important features are:

Produce a 'critical report' - this determines the format that is expected

Number of words 3,500 give or take 10% - this allows you to divide the paper into sections and get a feel for how many pages will be required.

Looking closely at the assessment task there are other important words of meaning:

Evidence of

Acquisition of knowledge, skills and understanding

Devise, plan, implement

Analyse, critically evaluate and synthesise

Small-scale piece of educational research.

Look for the concept words, the function words, and the scope of the task. It is so important to break the task down and equally to look at the task against the learning outcomes. In this instance the learning outcomes were:

Evidence a critical understanding of educational research planning and design

Critically analyse and evaluate a range of research methods and approaches used in educational research.

This now sets the scene to read and write... but does it, if you have never undertaken this type of task before?

The fundamentals of academic writing

The fundamentals of producing a paper begins very early on and can inevitably be determined by your engagement with the module. Every module provides a suggested reading list; some of those on the list are core reading and others are additional suggestions, but the reading does not stop there. 3 This could be referred to as a deep dive into the topic, but even the reading needs to be planned. There is no need to read everything you can find on the central topic, in fact the setting of your reading parameters will serve you well; 4 , 5 , 6 this is something discussed further on in the article.

Developing academic writing skills

No one style of writing fits all eventualities or all university conventions; academic English is more formal than much of the spoken language itself. There may be a need to become familiar with new technical terms and extend your vocabulary. 7

Academic writing can be:

Descriptive

Argumentative

The type of academic writing when addressing a university module is most likely to be pre-determined within the task, as is the structure. It can be useful to outline the sections and identify the word count per section. On average a paper of 2,500 words would have an introduction of approximately 200 words and a conclusion of 300 words, leaving 2,000 words for the body of the paper. Always confirm what is and what is not included in the word count.

Top tips for writing: 8 , 9 , 10

Make use of technology

Check the guidance on font size and style and line spacing

Read well; set parameters - consider the currency of the information, the source, the type of information, is it evidence-based, is there any bias. Consider counter arguments, differing perspectives. Build a scaffold and develop your own understanding. This will in turn inform your writing

If using numbers confirm that they can be trusted

Confirm if tables and pictures are allowed in the body of the text

Be aware of the required referencing style 11

Avoid plagiarism and reference as you go along

Develop a note-making style 12

Plan and structure the paper, identifying key milestones and timelines

Write your introduction last

Write words in full; avoid shorthand and acronyms

Be impersonal - avoid personal pronouns such as I/we/you; check if the third person is a requirement of the writing

Consider the sentence construction

Be objective

Write a first draft

Engage with module lead and act upon feedback

Write a final draft

Proofread before submitting and do not leave submitting the paper to the last minute.

Making broader use of these newfound skills can bring new and different opportunities and has the potential to arm you with the confidence to write in different spheres and contribute to debates and discussions. When it comes to writing for established journals you are likely to find that academic writing will have prepared you well.

Author information

Fiona is the President and Executive Director of the Society of British Dental Nurses and a member of the Dental Professional Alliance (DPA). She has received a British Empire Medal (BEM) and acts as a key opinion leader and advisor for oral health and preventative practice, infection prevention and professional practice. She has a strong interest in population level health matters and inequalities in health. Fiona is heavily involved in education across the sector and invests a great deal of time on programme design and development with a strong focus on quality assurance and assessment.

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Cottrell S. Study skills handbook (4th edition). Basingstoke: Palgrave MacMillan, 2013.

Burton N, Brundette M, Jones M. Doing your education research project . London: Sage, 2008.

Bell J. Doing your research project: a guide for first-time researchers in education, health and social science (5 th edition). Berkshire: Open University Press, 2010.

Day T. Success in academic writing (2 nd edition). Basingstoke: Palgrave MacMillan, 2018.

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In September BDJ Team will be publishing a themed issue focusing on DCP research. If you are a DCP, either in practice or studying at university, have been involved with research and would like to present your findings to the readers of BDJ Team , please contact the Editor via [email protected], or submit your article online at https://go.nature.com/31xft0w .

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(adapted from Research Paper Guide, Point Loma Nazarene University, 2010) 

Step 1: Be a Strategic Reader and Scholar 

Even before your paper is assigned, use the tools you have been given by your instructor and GSI, and create tools you can use later. 

See the handout “Be a Strategic Reader and Scholar” for more information.

Step 2: Understand the Assignment 

• Free topic choice or assigned?
• Type of paper: Informative? Persuasive? Other?
• Any terminology in assignment not clear?
• Library research needed or required? How much?
• What style of citation is required?
• Can you break the assignment into parts?
• When will you do each part?
• Are you required or allowed to collaborate with other members of the class?
• Other special directions or requirements?

Step 3: Select a Topic 

• interests you
• you know something about
• you can research easily
• Write out topic and brainstorm.
• Select your paper’s specific topic from this brainstorming list.
• In a sentence or short paragraph, describe what you think your paper is about.

Step 4: Initial Planning, Investigation, and Outlining 

• the nature of your audience
• ideas & information you already possess
• sources you can consult
• background reading you should do

Make a rough outline, a guide for your research to keep you on the subject while you work. 

Step 5: Accumulate Research Materials 

• Use cards, Word, Post-its, or Excel to organize.
• Organize your bibliography records first.
• Organize notes next (one idea per document— direct quotations, paraphrases, your own ideas).
• Arrange your notes under the main headings of your tentative outline. If necessary, print out documents and literally cut and paste (scissors and tape) them together by heading.

Step 6: Make a Final Outline to Guide Writing 

• Reorganize and fill in tentative outline.
• Organize notes to correspond to outline. 
• As you decide where you will use outside resources in your paper, make notes in your outline to refer to your numbered notecards, attach post-its to your printed outline, or note the use of outside resources in a different font or text color from the rest of your outline. 
• In both Steps 6 and 7, it is important to maintain a clear distinction between your own words and ideas and those of others.

Step 7: Write the Paper 

• Use your outline to guide you.
• Write quickly—capture flow of ideas—deal with proofreading later.
• Put aside overnight or longer, if possible.

Step 8: Revise and Proofread 

• Check organization—reorganize paragraphs and add transitions where necessary.
• Make sure all researched information is documented.
• Rework introduction and conclusion.
• Work on sentences—check spelling, punctuation, word choice, etc.
• Read out loud to check for flow.

Carolyn Swalina, Writing Program Coordinator  Student Learning Center, University of California, Berkeley ©2011 UC Regents

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

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Writing Survey Questions

Perhaps the most important part of the survey process is the creation of questions that accurately measure the opinions, experiences and behaviors of the public. Accurate random sampling will be wasted if the information gathered is built on a shaky foundation of ambiguous or biased questions. Creating good measures involves both writing good questions and organizing them to form the questionnaire.

Questionnaire design is a multistage process that requires attention to many details at once. Designing the questionnaire is complicated because surveys can ask about topics in varying degrees of detail, questions can be asked in different ways, and questions asked earlier in a survey may influence how people respond to later questions. Researchers are also often interested in measuring change over time and therefore must be attentive to how opinions or behaviors have been measured in prior surveys.

Surveyors may conduct pilot tests or focus groups in the early stages of questionnaire development in order to better understand how people think about an issue or comprehend a question. Pretesting a survey is an essential step in the questionnaire design process to evaluate how people respond to the overall questionnaire and specific questions, especially when questions are being introduced for the first time.

For many years, surveyors approached questionnaire design as an art, but substantial research over the past forty years has demonstrated that there is a lot of science involved in crafting a good survey questionnaire. Here, we discuss the pitfalls and best practices of designing questionnaires.

Question development

There are several steps involved in developing a survey questionnaire. The first is identifying what topics will be covered in the survey. For Pew Research Center surveys, this involves thinking about what is happening in our nation and the world and what will be relevant to the public, policymakers and the media. We also track opinion on a variety of issues over time so we often ensure that we update these trends on a regular basis to better understand whether people’s opinions are changing.

At Pew Research Center, questionnaire development is a collaborative and iterative process where staff meet to discuss drafts of the questionnaire several times over the course of its development. We frequently test new survey questions ahead of time through qualitative research methods such as  focus groups , cognitive interviews, pretesting (often using an  online, opt-in sample ), or a combination of these approaches. Researchers use insights from this testing to refine questions before they are asked in a production survey, such as on the ATP.

Measuring change over time

Many surveyors want to track changes over time in people’s attitudes, opinions and behaviors. To measure change, questions are asked at two or more points in time. A cross-sectional design surveys different people in the same population at multiple points in time. A panel, such as the ATP, surveys the same people over time. However, it is common for the set of people in survey panels to change over time as new panelists are added and some prior panelists drop out. Many of the questions in Pew Research Center surveys have been asked in prior polls. Asking the same questions at different points in time allows us to report on changes in the overall views of the general public (or a subset of the public, such as registered voters, men or Black Americans), or what we call “trending the data”.

When measuring change over time, it is important to use the same question wording and to be sensitive to where the question is asked in the questionnaire to maintain a similar context as when the question was asked previously (see  question wording  and  question order  for further information). All of our survey reports include a topline questionnaire that provides the exact question wording and sequencing, along with results from the current survey and previous surveys in which we asked the question.

The Center’s transition from conducting U.S. surveys by live telephone interviewing to an online panel (around 2014 to 2020) complicated some opinion trends, but not others. Opinion trends that ask about sensitive topics (e.g., personal finances or attending religious services ) or that elicited volunteered answers (e.g., “neither” or “don’t know”) over the phone tended to show larger differences than other trends when shifting from phone polls to the online ATP. The Center adopted several strategies for coping with changes to data trends that may be related to this change in methodology. If there is evidence suggesting that a change in a trend stems from switching from phone to online measurement, Center reports flag that possibility for readers to try to head off confusion or erroneous conclusions.

Open- and closed-ended questions

One of the most significant decisions that can affect how people answer questions is whether the question is posed as an open-ended question, where respondents provide a response in their own words, or a closed-ended question, where they are asked to choose from a list of answer choices.

For example, in a poll conducted after the 2008 presidential election, people responded very differently to two versions of the question: “What one issue mattered most to you in deciding how you voted for president?” One was closed-ended and the other open-ended. In the closed-ended version, respondents were provided five options and could volunteer an option not on the list.

When explicitly offered the economy as a response, more than half of respondents (58%) chose this answer; only 35% of those who responded to the open-ended version volunteered the economy. Moreover, among those asked the closed-ended version, fewer than one-in-ten (8%) provided a response other than the five they were read. By contrast, fully 43% of those asked the open-ended version provided a response not listed in the closed-ended version of the question. All of the other issues were chosen at least slightly more often when explicitly offered in the closed-ended version than in the open-ended version. (Also see  “High Marks for the Campaign, a High Bar for Obama”  for more information.)

Researchers will sometimes conduct a pilot study using open-ended questions to discover which answers are most common. They will then develop closed-ended questions based off that pilot study that include the most common responses as answer choices. In this way, the questions may better reflect what the public is thinking, how they view a particular issue, or bring certain issues to light that the researchers may not have been aware of.

When asking closed-ended questions, the choice of options provided, how each option is described, the number of response options offered, and the order in which options are read can all influence how people respond. One example of the impact of how categories are defined can be found in a Pew Research Center poll conducted in January 2002. When half of the sample was asked whether it was “more important for President Bush to focus on domestic policy or foreign policy,” 52% chose domestic policy while only 34% said foreign policy. When the category “foreign policy” was narrowed to a specific aspect – “the war on terrorism” – far more people chose it; only 33% chose domestic policy while 52% chose the war on terrorism.

In most circumstances, the number of answer choices should be kept to a relatively small number – just four or perhaps five at most – especially in telephone surveys. Psychological research indicates that people have a hard time keeping more than this number of choices in mind at one time. When the question is asking about an objective fact and/or demographics, such as the religious affiliation of the respondent, more categories can be used. In fact, they are encouraged to ensure inclusivity. For example, Pew Research Center’s standard religion questions include more than 12 different categories, beginning with the most common affiliations (Protestant and Catholic). Most respondents have no trouble with this question because they can expect to see their religious group within that list in a self-administered survey.

In addition to the number and choice of response options offered, the order of answer categories can influence how people respond to closed-ended questions. Research suggests that in telephone surveys respondents more frequently choose items heard later in a list (a “recency effect”), and in self-administered surveys, they tend to choose items at the top of the list (a “primacy” effect).

Because of concerns about the effects of category order on responses to closed-ended questions, many sets of response options in Pew Research Center’s surveys are programmed to be randomized to ensure that the options are not asked in the same order for each respondent. Rotating or randomizing means that questions or items in a list are not asked in the same order to each respondent. Answers to questions are sometimes affected by questions that precede them. By presenting questions in a different order to each respondent, we ensure that each question gets asked in the same context as every other question the same number of times (e.g., first, last or any position in between). This does not eliminate the potential impact of previous questions on the current question, but it does ensure that this bias is spread randomly across all of the questions or items in the list. For instance, in the example discussed above about what issue mattered most in people’s vote, the order of the five issues in the closed-ended version of the question was randomized so that no one issue appeared early or late in the list for all respondents. Randomization of response items does not eliminate order effects, but it does ensure that this type of bias is spread randomly.

Questions with ordinal response categories – those with an underlying order (e.g., excellent, good, only fair, poor OR very favorable, mostly favorable, mostly unfavorable, very unfavorable) – are generally not randomized because the order of the categories conveys important information to help respondents answer the question. Generally, these types of scales should be presented in order so respondents can easily place their responses along the continuum, but the order can be reversed for some respondents. For example, in one of Pew Research Center’s questions about abortion, half of the sample is asked whether abortion should be “legal in all cases, legal in most cases, illegal in most cases, illegal in all cases,” while the other half of the sample is asked the same question with the response categories read in reverse order, starting with “illegal in all cases.” Again, reversing the order does not eliminate the recency effect but distributes it randomly across the population.

Question wording

The choice of words and phrases in a question is critical in expressing the meaning and intent of the question to the respondent and ensuring that all respondents interpret the question the same way. Even small wording differences can substantially affect the answers people provide.

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An example of a wording difference that had a significant impact on responses comes from a January 2003 Pew Research Center survey. When people were asked whether they would “favor or oppose taking military action in Iraq to end Saddam Hussein’s rule,” 68% said they favored military action while 25% said they opposed military action. However, when asked whether they would “favor or oppose taking military action in Iraq to end Saddam Hussein’s rule  even if it meant that U.S. forces might suffer thousands of casualties, ” responses were dramatically different; only 43% said they favored military action, while 48% said they opposed it. The introduction of U.S. casualties altered the context of the question and influenced whether people favored or opposed military action in Iraq.

There has been a substantial amount of research to gauge the impact of different ways of asking questions and how to minimize differences in the way respondents interpret what is being asked. The issues related to question wording are more numerous than can be treated adequately in this short space, but below are a few of the important things to consider:

First, it is important to ask questions that are clear and specific and that each respondent will be able to answer. If a question is open-ended, it should be evident to respondents that they can answer in their own words and what type of response they should provide (an issue or problem, a month, number of days, etc.). Closed-ended questions should include all reasonable responses (i.e., the list of options is exhaustive) and the response categories should not overlap (i.e., response options should be mutually exclusive). Further, it is important to discern when it is best to use forced-choice close-ended questions (often denoted with a radio button in online surveys) versus “select-all-that-apply” lists (or check-all boxes). A 2019 Center study found that forced-choice questions tend to yield more accurate responses, especially for sensitive questions.  Based on that research, the Center generally avoids using select-all-that-apply questions.

It is also important to ask only one question at a time. Questions that ask respondents to evaluate more than one concept (known as double-barreled questions) – such as “How much confidence do you have in President Obama to handle domestic and foreign policy?” – are difficult for respondents to answer and often lead to responses that are difficult to interpret. In this example, it would be more effective to ask two separate questions, one about domestic policy and another about foreign policy.

In general, questions that use simple and concrete language are more easily understood by respondents. It is especially important to consider the education level of the survey population when thinking about how easy it will be for respondents to interpret and answer a question. Double negatives (e.g., do you favor or oppose  not  allowing gays and lesbians to legally marry) or unfamiliar abbreviations or jargon (e.g., ANWR instead of Arctic National Wildlife Refuge) can result in respondent confusion and should be avoided.

Similarly, it is important to consider whether certain words may be viewed as biased or potentially offensive to some respondents, as well as the emotional reaction that some words may provoke. For example, in a 2005 Pew Research Center survey, 51% of respondents said they favored “making it legal for doctors to give terminally ill patients the means to end their lives,” but only 44% said they favored “making it legal for doctors to assist terminally ill patients in committing suicide.” Although both versions of the question are asking about the same thing, the reaction of respondents was different. In another example, respondents have reacted differently to questions using the word “welfare” as opposed to the more generic “assistance to the poor.” Several experiments have shown that there is much greater public support for expanding “assistance to the poor” than for expanding “welfare.”

We often write two versions of a question and ask half of the survey sample one version of the question and the other half the second version. Thus, we say we have two  forms  of the questionnaire. Respondents are assigned randomly to receive either form, so we can assume that the two groups of respondents are essentially identical. On questions where two versions are used, significant differences in the answers between the two forms tell us that the difference is a result of the way we worded the two versions.

One of the most common formats used in survey questions is the “agree-disagree” format. In this type of question, respondents are asked whether they agree or disagree with a particular statement. Research has shown that, compared with the better educated and better informed, less educated and less informed respondents have a greater tendency to agree with such statements. This is sometimes called an “acquiescence bias” (since some kinds of respondents are more likely to acquiesce to the assertion than are others). This behavior is even more pronounced when there’s an interviewer present, rather than when the survey is self-administered. A better practice is to offer respondents a choice between alternative statements. A Pew Research Center experiment with one of its routinely asked values questions illustrates the difference that question format can make. Not only does the forced choice format yield a very different result overall from the agree-disagree format, but the pattern of answers between respondents with more or less formal education also tends to be very different.

One other challenge in developing questionnaires is what is called “social desirability bias.” People have a natural tendency to want to be accepted and liked, and this may lead people to provide inaccurate answers to questions that deal with sensitive subjects. Research has shown that respondents understate alcohol and drug use, tax evasion and racial bias. They also may overstate church attendance, charitable contributions and the likelihood that they will vote in an election. Researchers attempt to account for this potential bias in crafting questions about these topics. For instance, when Pew Research Center surveys ask about past voting behavior, it is important to note that circumstances may have prevented the respondent from voting: “In the 2012 presidential election between Barack Obama and Mitt Romney, did things come up that kept you from voting, or did you happen to vote?” The choice of response options can also make it easier for people to be honest. For example, a question about church attendance might include three of six response options that indicate infrequent attendance. Research has also shown that social desirability bias can be greater when an interviewer is present (e.g., telephone and face-to-face surveys) than when respondents complete the survey themselves (e.g., paper and web surveys).

Lastly, because slight modifications in question wording can affect responses, identical question wording should be used when the intention is to compare results to those from earlier surveys. Similarly, because question wording and responses can vary based on the mode used to survey respondents, researchers should carefully evaluate the likely effects on trend measurements if a different survey mode will be used to assess change in opinion over time.

Question order

Once the survey questions are developed, particular attention should be paid to how they are ordered in the questionnaire. Surveyors must be attentive to how questions early in a questionnaire may have unintended effects on how respondents answer subsequent questions. Researchers have demonstrated that the order in which questions are asked can influence how people respond; earlier questions can unintentionally provide context for the questions that follow (these effects are called “order effects”).

One kind of order effect can be seen in responses to open-ended questions. Pew Research Center surveys generally ask open-ended questions about national problems, opinions about leaders and similar topics near the beginning of the questionnaire. If closed-ended questions that relate to the topic are placed before the open-ended question, respondents are much more likely to mention concepts or considerations raised in those earlier questions when responding to the open-ended question.

For closed-ended opinion questions, there are two main types of order effects: contrast effects ( where the order results in greater differences in responses), and assimilation effects (where responses are more similar as a result of their order).

An example of a contrast effect can be seen in a Pew Research Center poll conducted in October 2003, a dozen years before same-sex marriage was legalized in the U.S. That poll found that people were more likely to favor allowing gays and lesbians to enter into legal agreements that give them the same rights as married couples when this question was asked after one about whether they favored or opposed allowing gays and lesbians to marry (45% favored legal agreements when asked after the marriage question, but 37% favored legal agreements without the immediate preceding context of a question about same-sex marriage). Responses to the question about same-sex marriage, meanwhile, were not significantly affected by its placement before or after the legal agreements question.

Another experiment embedded in a December 2008 Pew Research Center poll also resulted in a contrast effect. When people were asked “All in all, are you satisfied or dissatisfied with the way things are going in this country today?” immediately after having been asked “Do you approve or disapprove of the way George W. Bush is handling his job as president?”; 88% said they were dissatisfied, compared with only 78% without the context of the prior question.

Responses to presidential approval remained relatively unchanged whether national satisfaction was asked before or after it. A similar finding occurred in December 2004 when both satisfaction and presidential approval were much higher (57% were dissatisfied when Bush approval was asked first vs. 51% when general satisfaction was asked first).

Several studies also have shown that asking a more specific question before a more general question (e.g., asking about happiness with one’s marriage before asking about one’s overall happiness) can result in a contrast effect. Although some exceptions have been found, people tend to avoid redundancy by excluding the more specific question from the general rating.

Assimilation effects occur when responses to two questions are more consistent or closer together because of their placement in the questionnaire. We found an example of an assimilation effect in a Pew Research Center poll conducted in November 2008 when we asked whether Republican leaders should work with Obama or stand up to him on important issues and whether Democratic leaders should work with Republican leaders or stand up to them on important issues. People were more likely to say that Republican leaders should work with Obama when the question was preceded by the one asking what Democratic leaders should do in working with Republican leaders (81% vs. 66%). However, when people were first asked about Republican leaders working with Obama, fewer said that Democratic leaders should work with Republican leaders (71% vs. 82%).

The order questions are asked is of particular importance when tracking trends over time. As a result, care should be taken to ensure that the context is similar each time a question is asked. Modifying the context of the question could call into question any observed changes over time (see  measuring change over time  for more information).

A questionnaire, like a conversation, should be grouped by topic and unfold in a logical order. It is often helpful to begin the survey with simple questions that respondents will find interesting and engaging. Throughout the survey, an effort should be made to keep the survey interesting and not overburden respondents with several difficult questions right after one another. Demographic questions such as income, education or age should not be asked near the beginning of a survey unless they are needed to determine eligibility for the survey or for routing respondents through particular sections of the questionnaire. Even then, it is best to precede such items with more interesting and engaging questions. One virtue of survey panels like the ATP is that demographic questions usually only need to be asked once a year, not in each survey.

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Effective writing instruction for students in grades 6 to 12: a best evidence meta-analysis

• Published: 24 April 2024

Cite this article

• Steve Graham   ORCID: orcid.org/0000-0002-6702-5865 1 ,
• Yucheng Cao 2 ,
• Young-Suk Grace Kim 3 ,
• Joongwon Lee 4 ,
• Tamara Tate 3 ,
• Penelope Collins 3 ,
• Minkyung Cho 3 ,
• Youngsun Moon 3 ,
• Huy Quoc Chung 3 &
• Carol Booth Olson 3  

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The current best evidence meta-analysis reanalyzed the data from a meta-analysis by Graham et al. (J Educ Psychol 115:1004–1027, 2023). This meta-analysis and the prior one examined if teaching writing improved the writing of students in Grades 6 to 12, examining effects from writing intervention studies employing experimental and quasi-experimental designs (with pretests). In contrast to the prior meta-analysis, we eliminated all N of 1 treatment/control comparisons, studies with an attrition rate over 20%, studies that did not control for teacher effects, and studies that did not contain at least one reliable writing measure (0.70 or greater). Any writing outcome that was not reliable was also eliminated. Across 148 independent treatment/control comparisons, yielding 1,076 writing effect sizes (ESs) involving 22,838 students, teaching writing resulted in a positive and statistically detectable impact on students’ writing (ES = 0.38). Further, six of the 10 writing treatments tested in four or more independent comparisons improved students’ performance. This included the process approach to writing (0.75), strategy instruction (0.59), transcription instruction (0.54), feedback (0.30), pre-writing activities (0.32), and peer assistance (0.59). In addition, the Self-Regulated Strategy Development model for teaching writing strategies yielded a statistically significant ES of 0.84, whereas other approaches to teaching writing strategies resulted in a statistically significant ES of 0.51. The findings from this meta-analysis and the Graham et al. (2023) review which included studies that were methodologically weaker were compared. Implications for practice, research, and theory are presented.

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References marked with an asterisk indicate studies included in the meta-analysis.

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The research reported here was supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305C190007 to the University of California – Irvine for the WRITE Center. The opinions expressed are those of the authors and do not represent views of the Institute or the U.S. Department of Education.

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Graham, S., Cao, Y., Kim, YS.G. et al. Effective writing instruction for students in grades 6 to 12: a best evidence meta-analysis. Read Writ (2024). https://doi.org/10.1007/s11145-024-10539-2

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EDITORIAL article

Editorial: the brain in pain: a multidimensional approach.

• 1 Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
• 2 Psychology of Ageing Research Unit, Department of Developmental and Educational Psychology, University of Vienna, Vienna, Austria
• 3 Social, Cognitive and Affective Neuroscience Unit (SCAN-Unit), Department of Cognition, Emotion, and Methods in Psychology, University of Vienna, Vienna, Austria

Editorial on the Research Topic The brain in pain: a multidimensional approach

Introduction/background

The aim of the Research Topic “ The brain in pain: a multidisciplinary approach ” is to collect the latest quality research on the subject, focusing on the multiple facets of pain in humans, from its neural substrates to its possible expressions and modulation.

We have published papers by a total of 63 Authors, affiliated to research institutions located in five countries in three different continents, employing a variety of techniques, from behavioral, psychological and sensory testing, to Event-Related Potentials (ERPs), Near-InfraRed-Spectroscopy (NIRS) and functional Magnetic Resonance Imaging (fMRI), and including meta-analysis of previous research. Some of the studies dealt with specific chronic pain patients, others with different modulatory factors in healthy volunteers.

Overview of the articles in this Research Topic

Primary dysmenorrhea (PDM) is a very common cause of pelvic pain in women during their fertile years, with severe consequences for their quality of life ( Ferries-Rowe et al., 2020 ). The study by Lee et al. demonstrates that young Asian PDM females, who, differently from Caucasian PDM patients, do not express pain hypersensitivity, during acute noxious heat stimulation show reduced response and de-coupling of the Default Mode Network (DMN), but only in the painful menstrual phase. Another study by the same research group ( Hsu et al. ) reveals an influence of the A118G polymorphism of the OPRM1 gene on white matter features, especially of the motor network, but only during the painful menstrual phase, possibly with a maladaptive role. These results offer interesting contributions to the discussion of ethnic, genetic and hormonal influences both on pain perception and on its neural mechanisms.

Two studies focus on fibromyalgia (FM), another chronic pain condition, mostly affecting women ( Ruschak et al., 2023 ). Bao et al. investigated the relationship between fibromyalgia and long-term opioid use, revealing that, quite unexpectedly, temporal summation does not significantly change in FM patients, but negatively correlates with pain ratings, whereas higher opioid dosage correlates with higher heat pain sensitivity. On the other hand, Xin et al. performed a metanalysis of voxel-based morphometry (VBM) studies in FM, including updated data with respect to previous studies (see, e.g., Dehghan et al., 2016 ). They found changes in gray matter (GM) in FM patients, namely, increased GM in right postcentral gyrus and left angular gyrus, and decreased GM in right cingulate gyrus, right paracingulate gyrus, left cerebellum, and left gyrus rectus, i.e., brain regions involved in different (somatosensory, affective, cognitive) functions. These findings suggest both structural and functional adjustments, in a complex pain syndrome such as fibromyalgia.

Two more studies dealt with different forms of neuropathic pain: Du et al. adopted functional near-infrared spectroscopy (fNIRS) to detect cerebral changes in patients with cervical spondylosis. During acute pain stimulation, they found substantial increases in oxyhemoglobin concentrations in the frontal pole and dorsolateral prefrontal cortex, which significantly decreased in stimulation trials following analgesic procedures; these results add to our knowledge about the role of DLPFC in chronic pain conditions and about its potential as a therapeutic target ( Seminowicz and Moayedi, 2017 ). In the study by Bu et al. , spinal cord stimulation not only effectively reduced pain and other anomalies, such as sleep disorders, in patients affected by postherpetic neuralgia, but it also induced both static and dynamic brain resting state activity changes, which in some regions correlate with clinical characteristics.

Pain can be modulated by several factors, including physical exercise and training, although the underlying mechanisms are not yet well understood ( Lesnak and Sluka, 2020 ). Peier et al. compared endurance athletes to non-trained individuals, and identified a pain-resistant population, especially numerous among athletes, who show some peculiarities in their EEG pattern during pain perception (i.e., reduced global power spectra in the beta bands, as opposed to the increase found in non-resistant non-athletes); it is worth pointing out that the characterization of pain responses might lead to a personalized, and therefore more efficient, pain management.

Pain empathy is a powerful means to improve interpersonal communication and prosocial behavior (see, e.g., Smith et al., 2020 ). The study by Li et al. reveals ERP changes depending on the moral judgment given by the participant on the person experiencing pain, namely, smaller mean wave amplitude of positive 300 (P3) and late positive potential (LPP) for painful pictures of individuals deserving a low moral judgement, and vice versa for people deserving a high moral judgement; notably, the study puts these results in relationship with the issue of violence against healthcare operators, a reason of globally increasing alarm ( Banga et al., 2023 ).

Finally, two studies investigate the intriguing relationship between pain and language ( Borelli et al., 2021 ). Gilioli et al. investigated the electrophysiological correlates of implicit processing of words with pain content using an affective priming paradigm. The study indicates that valence and semantics of a stimulus interact to produce specific emotional responses. This research increases our knowledge of how pain-related words impact cognitive processing and emotional reactions, providing insights into the complex interplay among pain, affective priming, and cognitive mechanisms. Lastly, the study by Borelli et al. presents an event-related fMRI study that aimed to compare brain activity related to perceiving nociceptive pain and processing semantic pain, and specifically, words related to either physical or social pain. The results show that words associated with social pain activate regions linked to affective-motivational aspects of pain perception; conversely, words related to physical pain trigger activity in regions associated with sensory-discriminative aspects of pain perception; the degree of activation in specific regions vary depending on the type of pain being processed. This study sheds light on how words associated with physical and social pain influence the brain networks involved in pain perception.

In conclusion, the present Research Topic, “ The brain in pain: a multidimensional approach ”, brings together cutting-edge research and diverse perspectives to increase our understanding of how the brain perceives, processes, and responds to pain. By doing so, it both advances our knowledge on the neuroscience of pain, and offers new perspectives for innovative approaches to pain management and treatment.

Author contributions

FB: Writing—review & editing, Writing—original draft, Project administration, Conceptualization. AM-H: Writing—review & editing, Project administration, Conceptualization. CAP: Writing—review & editing, Supervision, Conceptualization. FL: Writing—review & editing, Writing—original draft, Supervision, Project administration, Conceptualization.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Banga, A., Mautong, H., Alamoudi, R., Faisal, U. H., Bhatt, G., Amal, T., et al. (2023). ViSHWaS: violence study of healthcare workers and systems-a global survey. BMJ Glob. Health. 8, e013101. doi: 10.1136/bmjgh-2023-013101

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Borelli, E., Bigi, S., Potenza, L., Artioli, F., Eliardo, S., Mucciarini, C., et al. (2021). Different semantic and affective meaning of the words associated to physical and social pain in cancer patients on early palliative/supportive care and in healthy, pain-free individuals. PLoS ONE . 16, e0248755. doi: 10.1371/journal.pone.0248755

Dehghan, M., Schmidt-Wilcke, T., Pfleiderer, B., Eickhoff, S. B., Petzke, F., Harris, R. E., et al. (2016). Coordinate-based (ALE) meta-analysis of brain activation in patients with fibromyalgia. Hum. Brain Mapp . 37, 1749–1758. doi: 10.1002/hbm.23132

Ferries-Rowe, E., Corey, E., and Archer, J. S. (2020). Primary dysmenorrhea: diagnosis and therapy. Obstet. Gynecol . 136, 1047–1058. doi: 10.1097/AOG.0000000000004096

Crossref Full Text | Google Scholar

Lesnak, J. B., and Sluka, K. A. (2020). Mechanism of exercise-induced analgesia: what we can learn from physically active animals. PAIN Reports 5, e850. doi: 10.1097/PR9.0000000000000850

Ruschak, I., Montesó-Curto, P., Rosselló, L., Aguilar Martín, C., Sánchez-Montesó, L., and Toussaint, L. (2023). Fibromyalgia syndrome pain in men and women: a scoping review. Healthcare 11, 223. doi: 10.3390/healthcare11020223

Seminowicz, D. A., and Moayedi, M. (2017). The dorsolateral prefrontal cortex in acute and chronic pain. J Pain . 18, 1027–1035. doi: 10.1016/j.jpain.2017.03.008

Smith, K. E., Norman, G. J., and Decety, J. (2020). Medical students' empathy positively predicts charitable donation behavior. J. Posit. Psychol. 15, 734–742. doi: 10.1080/17439760.2019.1651889

Keywords: pain, empathy, Event-Related Potentials (ERPs), Near-InfraRed-Spectroscopy (NIRS), Magnetic Resonance Imaging (MRI), meta-analysis

Citation: Benuzzi F, Müllner-Huber A, Porro CA and Lui F (2024) Editorial: The brain in pain: a multidimensional approach. Front. Psychol. 15:1401784. doi: 10.3389/fpsyg.2024.1401784

Received: 15 March 2024; Accepted: 25 March 2024; Published: 17 April 2024.

Edited and reviewed by: Lars Muckli , University of Glasgow, United Kingdom

Copyright © 2024 Benuzzi, Müllner-Huber, Porro and Lui. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Fausta Lui, fausta.lui@unimore.it

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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How to write better ChatGPT prompts in 5 steps

ChatGPT is the generative artificial intelligence (AI) tool that's taken the world by storm. While there's always the possibility it will simply make stuff up , there's a lot you can do when crafting prompts to ensure the best possible outcome. That's what we'll be exploring in this how-to.

In this article, we'll show you how to write prompts that encourage the large language model (LLM) that powers  ChatGPT to provide the best possible answers. 

Also: Have 10 hours? IBM will train you in AI fundamentals - for free

Writing effective prompts, known as prompt engineering, has even become its own highly-paid discipline . Who knows? These tips could help you build the skills to become one of those highly paid prompt engineers. Apparently, these gigs can pay from $175,000 to $335,000 per year.  

How to write effective ChatGPT prompts

1. talk to the ai like you would a person.

One of the more interesting things I had to get used to when working with ChatGPT is that you don't program it, you talk to it. As a formally trained programmer, I've had to leave a lot of habits by the wayside when engaging with AI. Talking to it (and with it) requires a mindset shift.

When I say talk to it like a person, I mean talk to it like you would a co-worker or team member. If that's hard to do, give it a name. Alexa is taken, so maybe think of it as "Bob". This naming helps because when you talk to Bob, you might include conversational details, little anecdotes that give your story texture.

Also:   How to use ChatGPT to write code

When talking to a person, it would be natural for them to miss your point initially and require clarification, or veer away from the topic at hand and need to be wrangled back. You might need to fill in the backstory for them, or restate complex questions based on the answers they give you. 

This is called interactive prompting. Don't be afraid to ask multi-step questions: ask, get a response, and based on that response, ask another question. I've done this myself, sometimes 10 or 20 times in a row, and gotten very powerful results. Think of this as having a conversation with ChatGPT.

2. Set the stage and provide context

Writing a ChatGPT prompt is more than just asking a one-sentence question. It often involves providing relevant background information to set the context of the query.

Let's say that you want to prepare for a marathon (for the record, I do not run, dance, or jump -- this is merely an example). You could ask ChatGPT:

How can I prepare for a marathon?

However, you'll get a far more nuanced answer if you add that you're training for your first marathon. Try this instead: 

I am a beginner runner and have never run a marathon before, but I want to complete one in six months. How can I prepare for a marathon?

By giving the AI more information, you're helping it return a more focused answer. Even with ChatGPT's help, there's no way I'm going to run a marathon (unless I'm doing it with a V-Twin motor under my seat). Here are two more examples of questions that provide context:

I am planning to travel to Spain in a few months and would like to learn some basic Spanish to help me communicate with local residents. I am looking for online resources that are suitable for beginners and provide a structured and comprehensive approach to learning the language. Can you recommend some online resources for learning Spanish as a beginner?

In this case, rather than just asking about learning resources, the context helps focus the AI on learning how to communicate on the ground with local residents. Here's another example: 

I am a business owner interested in exploring how blockchain technology can be used to improve supply chain efficiency and transparency. I am looking for a clear and concise explanation of the technology and examples of how it has been used in the context of supply chain management. Can you explain the concept of blockchain technology and its potential applications in supply chain management?

In this example, rather than just asking for information on blockchain and how it works, the focus is specifically on blockchain for supply chain efficiency and how it might be used in a real-world scenario. 

Also:  How to use Image Creator from Microsoft Designer (formerly Bing Image Creator) Lastly, let's get into how to construct a detailed prompt. 

One note: I limit the answer to 500 words because ChatGPT seems to break when asked to produce somewhere between 500 and 700 words, leaving stories mid-sentence and not resuming properly when asked to continue. I hope future versions provide longer answers, because premises like this can generate fun story beginnings: 

Write a short story for me, no more than 500 words. The story takes place in 2339, in Boston. The entire story takes place inside a Victorian-style bookstore that wouldn't be out of place in Diagon Alley. Inside the store are the following characters, all human: The proprietor: make this person interesting and a bit unusual, give them a name and at least one skill or characteristic that influences their backstory and possibly influences the entire short story. The helper: this is a clerk in the store. His name is Todd. The customer and his friend: Two customers came into the store together, Jackson and Ophelia. Jackson is dressed as if he's going to a Steampunk convention, while Ophelia is clearly coming home from her day working in a professional office. Another customer is Evangeline, a regular customer in the store, in her mid-40s. Yet another customer is Archibald, a man who could be anywhere from 40 to 70 years old. He has a mysterious air about himself and seems both somewhat grandiose and secretive. There is something about Archibald that makes the others uncomfortable. A typical concept in retail sales is that there's always more inventory "in the back," where there's a storeroom for additional goods that might not be shown on the shelves where customers browse. The premise of this story is that there is something very unusual about this store's "in the back." Put it all together and tell something compelling and fun.

You can see how the detail provides more for the AI to work with. First, feed "Write me a story about a bookstore" into ChatGPT and see what it gives you. Then feed in the above prompt and you'll see the difference.

3. Tell the AI to assume an identity or profession

One of ChatGPT's coolest features is that it can write from the point of view of a specific person or profession. In a previous article, I showed how you can make ChatGPT write like a pirate or Shakespeare , but you can also have it write like a teacher, a marketing executive, a fiction writer -- anyone you want. 

Also: How ChatGPT can rewrite and improve your existing code  

For example, I can ask ChatGPT to describe the Amazon Echo smart home device, but to do so from the point of view of a product manager, a caregiver, and a journalist in three separate prompts: 

From the point of view of its product manager, describe the Amazon Echo Alexa device. From the point of view of an adult child caring for an elderly parent, describe the Amazon Echo Alexa device. From the point of view of a journalist, describe the Amazon Echo Alexa device.

Try dropping these three prompts into ChatGPT to see its complete response. 

I've pulled a few lines from ChatGPT's responses, so you can see how it interprets different perspectives.  From the product manager identity:  I can confidently say that this is one of the most innovative and revolutionary products in the smart home industry.

From the caregiver identity:  The device's ability to set reminders and alarms can be particularly helpful for elderly individuals who may have trouble remembering to take their medication or attend appointments.

Also:   5 ways to explore the use of generative AI at work

And from the journalist identity:  From a journalistic perspective, the Echo has made headlines due to privacy concerns surrounding the collection and storage of user data.

You can see how different identities allow the AI to provide different perspectives as part of its response. To expand this, you can let the AI do a thought experiment. Let's look at some of the issues that went into the creation of something like Alexa:

The year is 2012. Siri has been out for the iPhone for about a year, but nothing like an Alexa smart home device has been released. The scene is an Amazon board meeting where the Echo smart assistant based on Alexa has just been proposed.  Provide the arguments, pro and con, that board members at that meeting would have been likely to discuss as part of their process of deciding whether or not to approve spending to invest in developing the device.  Feel free to also include participation by engineering design experts and product champions, if that provides more comprehensive perspective.

It's also good to know that making minor changes to your prompts can significantly change ChatGPT's response. For example, when I changed the phrase, "Provide the arguments, pro and con, that..." to "Provide the pro and con arguments as dialogue, that...," ChatGPT rewrote its answer, switching from a list of enumerated pros and cons to an actual dialogue between participants.

4. Keep ChatGPT on track

As mentioned above, ChatGPT has a tendency to go off the rails, lose track of the discussion, or completely fabricate answers. 

There are a few techniques you can use to help keep it on track and honest.

One of my favorite things to do is ask ChatGPT to justify its responses. I'll use phrases like "Why do you think that?" or "What evidence supports your answer?" Often, the AI will simply apologize for making stuff up and come back with a new answer. Other times, it might give you some useful information about its reasoning path. In any case, don't forget to apply the tips I provide for having ChatGPT cite sources .

Also:  My two favorite ChatGPT Plus features and the remarkable things I can do with them

If you have a fairly long conversation with ChatGPT, you'll start to notice that the AI loses the thread. Not that that's unique to AIs -- even in extended conversations with humans, someone is bound to get lost. That said, you can gently guide the AI back on track by reminding it what the topic is, as well as what you're trying to explore.

5. Don't be afraid to play and experiment

One of the best ways to up your skill at this craft is to play around with what the chatbot can do.

Try feeding ChatGPT a variety of interesting prompts to see what it will do with them. Then change them up and see what happens. Here are five to get you started:

• Imagine you are a raindrop falling from the sky during a thunderstorm. Describe your journey from the moment you form in the cloud to the moment you hit the ground. What do you see, feel, and experience?
• You are a toy that has been left behind in an attic for decades. Narrate your feelings, memories of playtimes past, and your hopes of being rediscovered.
• Write the final diary entry of a time traveler who has decided to settle down in a specific era, explaining why they chose that time and what they've learned from their travels.
• Imagine a dialogue between two unlikely objects, like a teacup and a wristwatch, discussing the daily routines and challenges they face.
• Describe a day in an ant colony from the perspective of an ant. Dive deep into the politics, challenges, and social structures of the ant world.

Pay attention not only to what the AI generates, but how it generates what it does, what mistakes it makes, and where it seems to run into limits. All of that detail will help you expand your prompting horizons.

More prompt-writing tips 

• Feel free to re-ask the question. ChatGPT will often change its answer with each ask.
• Make small changes to your prompts to guide it into giving you a better answer.
• ChatGPT will retain its awareness of previous conversations as long as the current page is open. If you leave that page, it will lose awareness. To be clear, ChatGPT will also sometimes lose the thread of the conversation without reason, so be aware you may need to start over from time to time.
• Similarly, opening a new page will start the discussion with fresh responses.
• Be sure to specify the length of the response you want. Answers over about 500 words sometimes break down. 
• You can correct and clarify prompts based on how the AI answered previously. If it's misinterpreting you, you may be able to just tell it what it missed and continue.
• Rephrase questions if ChatGPT doesn't want to answer what you're asking. Use personas to elicit answers that it might not otherwise want to give.
• If you want sources cited , tell it to support or justify its answers.
• ChatGPT custom instructions are now available to free users. You can  give ChatGPT a set of prompts that are always available , so you don't have to retype them.
• Keep experimenting.
• Consider getting the ChatGPT Plus subscription . You can then use your own data for powerful analytics . You can also pull data from the Web . 
• Try asking the same question of Gemini  (formerly Bard) or Copilot (formerly Bing Chat). Both will interpret your prompts differently and answer differently. This is effectively getting a second opinion on your prompt, and can give you alternate perspectives.
• Ask for examples. If you want to see how well ChatGPT understands what you're asking for, ask it "Can you give me three examples of how that works?" or similar questions.
• Ask it to repeat parts of your original requests back to you. For example, if you feed it an article to analyze, you can tell it something like, "Just to be sure you understand, please echo back the first three headlines," or "I want to be sure you understand what I mean, so summarize the main conflict discussed in this article." 
• Sometimes ChatGPT just fails. Keep trying, but also be willing to give up and move on to other tools. It's not perfect...yet.

What type of prompts work best with ChatGPT? 

Part of what makes ChatGPT so compelling is you can ask it almost anything. That said, keep in mind that it's designed to provide written answers. If you want a list of websites, you're better off talking to Google. 

Also:  How to use DALL-E 3 in ChatGPT

If you want some form of computation, talk to Wolfram Alpha . Give ChatGPT open-ended prompts, encourage creativity, and don't be afraid to share personal experiences or emotions. Plus, keep in mind that the AI's knowledge ends in 2021  for ChatGPT 3.5 and December 2023 for ChatGPT 4 in ChatGPT Plus.

How can I adjust the complexity of ChatGPT responses?

You can directly specify the complexity level by including it in your prompt. Add "... at a high school level" or "... at a level intended for a Ph.D. to understand" to the end of your question. You can also increase complexity of output by increasing the richness of your input. The more you provide in your prompt, the more detailed and nuanced ChatGPT's response will be. You can also include other specific instructions, like "Give me a summary," "Explain in detail," or "Provide a technical description."

Also:  How does ChatGPT actually work?

You can also pre-define profiles. For example, you could say "When evaluating something for a manager, assume an individual with a four-year business college education, a lack of detailed technical understanding, and a fairly limited attention span, who likes to get answers that are clear and concise. When evaluating something for a programmer, assume considerable technical knowledge, an enjoyment of geek and science fiction references, and a desire for a complete answer. Accuracy is deeply important to programmers, so double-check your work."

If you ask ChatGPT to "explain C++ to a manager" and "explain C++ to a programmer," you'll see how the responses differ.

What do I do if ChatGPT refuses to answer or I don't like its answer? 

There are some guardrails built into ChatGPT. It tends to shut down if you ask it political questions, for example. That's what's built into the system. While you might be able to tease out an answer, it's probably not going to provide great value. That said, feel free to keep trying with different phrasing or perspectives. 

You can follow my day-to-day project updates on social media. Be sure to subscribe to my weekly update newsletter on Substack , and follow me on Twitter at @DavidGewirtz , on Facebook at Facebook.com/DavidGewirtz , on Instagram at Instagram.com/DavidGewirtz , and on YouTube at YouTube.com/DavidGewirtzTV .

More on AI tools

Google releases two new free resources to help you optimize your ai prompts, my top 5 tools to beat distractions and get more writing done - or any work, really, create your own e-book using ai for just $25.

Our approach

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RECOMMENDED READS

• 5 Steps to Getting Started with Llama 2
• The Llama Ecosystem: Past, Present, and Future
• Introducing Code Llama, a state-of-the-art large language model for coding
• Meta and Microsoft Introduce the Next Generation of Llama
• Today, we’re introducing Meta Llama 3, the next generation of our state-of-the-art open source large language model.
• Llama 3 models will soon be available on AWS, Databricks, Google Cloud, Hugging Face, Kaggle, IBM WatsonX, Microsoft Azure, NVIDIA NIM, and Snowflake, and with support from hardware platforms offered by AMD, AWS, Dell, Intel, NVIDIA, and Qualcomm.
• We’re dedicated to developing Llama 3 in a responsible way, and we’re offering various resources to help others use it responsibly as well. This includes introducing new trust and safety tools with Llama Guard 2, Code Shield, and CyberSec Eval 2.
• In the coming months, we expect to introduce new capabilities, longer context windows, additional model sizes, and enhanced performance, and we’ll share the Llama 3 research paper.
• Meta AI, built with Llama 3 technology, is now one of the world’s leading AI assistants that can boost your intelligence and lighten your load—helping you learn, get things done, create content, and connect to make the most out of every moment. You can try Meta AI here .

Today, we’re excited to share the first two models of the next generation of Llama, Meta Llama 3, available for broad use. This release features pretrained and instruction-fine-tuned language models with 8B and 70B parameters that can support a broad range of use cases. This next generation of Llama demonstrates state-of-the-art performance on a wide range of industry benchmarks and offers new capabilities, including improved reasoning. We believe these are the best open source models of their class, period. In support of our longstanding open approach, we’re putting Llama 3 in the hands of the community. We want to kickstart the next wave of innovation in AI across the stack—from applications to developer tools to evals to inference optimizations and more. We can’t wait to see what you build and look forward to your feedback.

Our goals for Llama 3

With Llama 3, we set out to build the best open models that are on par with the best proprietary models available today. We wanted to address developer feedback to increase the overall helpfulness of Llama 3 and are doing so while continuing to play a leading role on responsible use and deployment of LLMs. We are embracing the open source ethos of releasing early and often to enable the community to get access to these models while they are still in development. The text-based models we are releasing today are the first in the Llama 3 collection of models. Our goal in the near future is to make Llama 3 multilingual and multimodal, have longer context, and continue to improve overall performance across core LLM capabilities such as reasoning and coding.

State-of-the-art performance

Our new 8B and 70B parameter Llama 3 models are a major leap over Llama 2 and establish a new state-of-the-art for LLM models at those scales. Thanks to improvements in pretraining and post-training, our pretrained and instruction-fine-tuned models are the best models existing today at the 8B and 70B parameter scale. Improvements in our post-training procedures substantially reduced false refusal rates, improved alignment, and increased diversity in model responses. We also saw greatly improved capabilities like reasoning, code generation, and instruction following making Llama 3 more steerable.

*Please see evaluation details for setting and parameters with which these evaluations are calculated.

In the development of Llama 3, we looked at model performance on standard benchmarks and also sought to optimize for performance for real-world scenarios. To this end, we developed a new high-quality human evaluation set. This evaluation set contains 1,800 prompts that cover 12 key use cases: asking for advice, brainstorming, classification, closed question answering, coding, creative writing, extraction, inhabiting a character/persona, open question answering, reasoning, rewriting, and summarization. To prevent accidental overfitting of our models on this evaluation set, even our own modeling teams do not have access to it. The chart below shows aggregated results of our human evaluations across of these categories and prompts against Claude Sonnet, Mistral Medium, and GPT-3.5.

Preference rankings by human annotators based on this evaluation set highlight the strong performance of our 70B instruction-following model compared to competing models of comparable size in real-world scenarios.

Our pretrained model also establishes a new state-of-the-art for LLM models at those scales.

To develop a great language model, we believe it’s important to innovate, scale, and optimize for simplicity. We adopted this design philosophy throughout the Llama 3 project with a focus on four key ingredients: the model architecture, the pretraining data, scaling up pretraining, and instruction fine-tuning.

Model architecture

In line with our design philosophy, we opted for a relatively standard decoder-only transformer architecture in Llama 3. Compared to Llama 2, we made several key improvements. Llama 3 uses a tokenizer with a vocabulary of 128K tokens that encodes language much more efficiently, which leads to substantially improved model performance. To improve the inference efficiency of Llama 3 models, we’ve adopted grouped query attention (GQA) across both the 8B and 70B sizes. We trained the models on sequences of 8,192 tokens, using a mask to ensure self-attention does not cross document boundaries.

Training data

To train the best language model, the curation of a large, high-quality training dataset is paramount. In line with our design principles, we invested heavily in pretraining data. Llama 3 is pretrained on over 15T tokens that were all collected from publicly available sources. Our training dataset is seven times larger than that used for Llama 2, and it includes four times more code. To prepare for upcoming multilingual use cases, over 5% of the Llama 3 pretraining dataset consists of high-quality non-English data that covers over 30 languages. However, we do not expect the same level of performance in these languages as in English.

To ensure Llama 3 is trained on data of the highest quality, we developed a series of data-filtering pipelines. These pipelines include using heuristic filters, NSFW filters, semantic deduplication approaches, and text classifiers to predict data quality. We found that previous generations of Llama are surprisingly good at identifying high-quality data, hence we used Llama 2 to generate the training data for the text-quality classifiers that are powering Llama 3.

We also performed extensive experiments to evaluate the best ways of mixing data from different sources in our final pretraining dataset. These experiments enabled us to select a data mix that ensures that Llama 3 performs well across use cases including trivia questions, STEM, coding, historical knowledge, etc.

Scaling up pretraining

To effectively leverage our pretraining data in Llama 3 models, we put substantial effort into scaling up pretraining. Specifically, we have developed a series of detailed scaling laws for downstream benchmark evaluations. These scaling laws enable us to select an optimal data mix and to make informed decisions on how to best use our training compute. Importantly, scaling laws allow us to predict the performance of our largest models on key tasks (for example, code generation as evaluated on the HumanEval benchmark—see above) before we actually train the models. This helps us ensure strong performance of our final models across a variety of use cases and capabilities.

We made several new observations on scaling behavior during the development of Llama 3. For example, while the Chinchilla-optimal amount of training compute for an 8B parameter model corresponds to ~200B tokens, we found that model performance continues to improve even after the model is trained on two orders of magnitude more data. Both our 8B and 70B parameter models continued to improve log-linearly after we trained them on up to 15T tokens. Larger models can match the performance of these smaller models with less training compute, but smaller models are generally preferred because they are much more efficient during inference.

To train our largest Llama 3 models, we combined three types of parallelization: data parallelization, model parallelization, and pipeline parallelization. Our most efficient implementation achieves a compute utilization of over 400 TFLOPS per GPU when trained on 16K GPUs simultaneously. We performed training runs on two custom-built 24K GPU clusters . To maximize GPU uptime, we developed an advanced new training stack that automates error detection, handling, and maintenance. We also greatly improved our hardware reliability and detection mechanisms for silent data corruption, and we developed new scalable storage systems that reduce overheads of checkpointing and rollback. Those improvements resulted in an overall effective training time of more than 95%. Combined, these improvements increased the efficiency of Llama 3 training by ~three times compared to Llama 2.

Instruction fine-tuning

To fully unlock the potential of our pretrained models in chat use cases, we innovated on our approach to instruction-tuning as well. Our approach to post-training is a combination of supervised fine-tuning (SFT), rejection sampling, proximal policy optimization (PPO), and direct preference optimization (DPO). The quality of the prompts that are used in SFT and the preference rankings that are used in PPO and DPO has an outsized influence on the performance of aligned models. Some of our biggest improvements in model quality came from carefully curating this data and performing multiple rounds of quality assurance on annotations provided by human annotators.

Learning from preference rankings via PPO and DPO also greatly improved the performance of Llama 3 on reasoning and coding tasks. We found that if you ask a model a reasoning question that it struggles to answer, the model will sometimes produce the right reasoning trace: The model knows how to produce the right answer, but it does not know how to select it. Training on preference rankings enables the model to learn how to select it.

Building with Llama 3

Our vision is to enable developers to customize Llama 3 to support relevant use cases and to make it easier to adopt best practices and improve the open ecosystem. With this release, we’re providing new trust and safety tools including updated components with both Llama Guard 2 and Cybersec Eval 2, and the introduction of Code Shield—an inference time guardrail for filtering insecure code produced by LLMs.

We’ve also co-developed Llama 3 with torchtune , the new PyTorch-native library for easily authoring, fine-tuning, and experimenting with LLMs. torchtune provides memory efficient and hackable training recipes written entirely in PyTorch. The library is integrated with popular platforms such as Hugging Face, Weights & Biases, and EleutherAI and even supports Executorch for enabling efficient inference to be run on a wide variety of mobile and edge devices. For everything from prompt engineering to using Llama 3 with LangChain we have a comprehensive getting started guide and takes you from downloading Llama 3 all the way to deployment at scale within your generative AI application.

A system-level approach to responsibility

We have designed Llama 3 models to be maximally helpful while ensuring an industry leading approach to responsibly deploying them. To achieve this, we have adopted a new, system-level approach to the responsible development and deployment of Llama. We envision Llama models as part of a broader system that puts the developer in the driver’s seat. Llama models will serve as a foundational piece of a system that developers design with their unique end goals in mind.

Instruction fine-tuning also plays a major role in ensuring the safety of our models. Our instruction-fine-tuned models have been red-teamed (tested) for safety through internal and external efforts. ​​Our red teaming approach leverages human experts and automation methods to generate adversarial prompts that try to elicit problematic responses. For instance, we apply comprehensive testing to assess risks of misuse related to Chemical, Biological, Cyber Security, and other risk areas. All of these efforts are iterative and used to inform safety fine-tuning of the models being released. You can read more about our efforts in the model card .

Llama Guard models are meant to be a foundation for prompt and response safety and can easily be fine-tuned to create a new taxonomy depending on application needs. As a starting point, the new Llama Guard 2 uses the recently announced MLCommons taxonomy, in an effort to support the emergence of industry standards in this important area. Additionally, CyberSecEval 2 expands on its predecessor by adding measures of an LLM’s propensity to allow for abuse of its code interpreter, offensive cybersecurity capabilities, and susceptibility to prompt injection attacks (learn more in our technical paper ). Finally, we’re introducing Code Shield which adds support for inference-time filtering of insecure code produced by LLMs. This offers mitigation of risks around insecure code suggestions, code interpreter abuse prevention, and secure command execution.

With the speed at which the generative AI space is moving, we believe an open approach is an important way to bring the ecosystem together and mitigate these potential harms. As part of that, we’re updating our Responsible Use Guide (RUG) that provides a comprehensive guide to responsible development with LLMs. As we outlined in the RUG, we recommend that all inputs and outputs be checked and filtered in accordance with content guidelines appropriate to the application. Additionally, many cloud service providers offer content moderation APIs and other tools for responsible deployment, and we encourage developers to also consider using these options.

Deploying Llama 3 at scale

Llama 3 will soon be available on all major platforms including cloud providers, model API providers, and much more. Llama 3 will be everywhere .

Our benchmarks show the tokenizer offers improved token efficiency, yielding up to 15% fewer tokens compared to Llama 2. Also, Group Query Attention (GQA) now has been added to Llama 3 8B as well. As a result, we observed that despite the model having 1B more parameters compared to Llama 2 7B, the improved tokenizer efficiency and GQA contribute to maintaining the inference efficiency on par with Llama 2 7B.

For examples of how to leverage all of these capabilities, check out Llama Recipes which contains all of our open source code that can be leveraged for everything from fine-tuning to deployment to model evaluation.

What’s next for Llama 3?

The Llama 3 8B and 70B models mark the beginning of what we plan to release for Llama 3. And there’s a lot more to come.

Our largest models are over 400B parameters and, while these models are still training, our team is excited about how they’re trending. Over the coming months, we’ll release multiple models with new capabilities including multimodality, the ability to converse in multiple languages, a much longer context window, and stronger overall capabilities. We will also publish a detailed research paper once we are done training Llama 3.

To give you a sneak preview for where these models are today as they continue training, we thought we could share some snapshots of how our largest LLM model is trending. Please note that this data is based on an early checkpoint of Llama 3 that is still training and these capabilities are not supported as part of the models released today.

We’re committed to the continued growth and development of an open AI ecosystem for releasing our models responsibly. We have long believed that openness leads to better, safer products, faster innovation, and a healthier overall market. This is good for Meta, and it is good for society. We’re taking a community-first approach with Llama 3, and starting today, these models are available on the leading cloud, hosting, and hardware platforms with many more to come.

Try Meta Llama 3 today

We’ve integrated our latest models into Meta AI, which we believe is the world’s leading AI assistant. It’s now built with Llama 3 technology and it’s available in more countries across our apps.

You can use Meta AI on Facebook, Instagram, WhatsApp, Messenger, and the web to get things done, learn, create, and connect with the things that matter to you. You can read more about the Meta AI experience here .

Visit the Llama 3 website to download the models and reference the Getting Started Guide for the latest list of all available platforms.

You’ll also soon be able to test multimodal Meta AI on our Ray-Ban Meta smart glasses.

As always, we look forward to seeing all the amazing products and experiences you will build with Meta Llama 3.

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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.