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How to Write and Publish a Research Paper for a Peer-Reviewed Journal

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• Published: 30 April 2020
• Volume 36 , pages 909–913, ( 2021 )

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• Clara Busse   ORCID: orcid.org/0000-0002-0178-1000 1 &
• Ella August   ORCID: orcid.org/0000-0001-5151-1036 1 , 2  

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Communicating research findings is an essential step in the research process. Often, peer-reviewed journals are the forum for such communication, yet many researchers are never taught how to write a publishable scientific paper. In this article, we explain the basic structure of a scientific paper and describe the information that should be included in each section. We also identify common pitfalls for each section and recommend strategies to avoid them. Further, we give advice about target journal selection and authorship. In the online resource 1 , we provide an example of a high-quality scientific paper, with annotations identifying the elements we describe in this article.

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Introduction

Writing a scientific paper is an important component of the research process, yet researchers often receive little formal training in scientific writing. This is especially true in low-resource settings. In this article, we explain why choosing a target journal is important, give advice about authorship, provide a basic structure for writing each section of a scientific paper, and describe common pitfalls and recommendations for each section. In the online resource 1 , we also include an annotated journal article that identifies the key elements and writing approaches that we detail here. Before you begin your research, make sure you have ethical clearance from all relevant ethical review boards.

Select a Target Journal Early in the Writing Process

We recommend that you select a “target journal” early in the writing process; a “target journal” is the journal to which you plan to submit your paper. Each journal has a set of core readers and you should tailor your writing to this readership. For example, if you plan to submit a manuscript about vaping during pregnancy to a pregnancy-focused journal, you will need to explain what vaping is because readers of this journal may not have a background in this topic. However, if you were to submit that same article to a tobacco journal, you would not need to provide as much background information about vaping.

Information about a journal’s core readership can be found on its website, usually in a section called “About this journal” or something similar. For example, the Journal of Cancer Education presents such information on the “Aims and Scope” page of its website, which can be found here: https://www.springer.com/journal/13187/aims-and-scope .

Peer reviewer guidelines from your target journal are an additional resource that can help you tailor your writing to the journal and provide additional advice about crafting an effective article [ 1 ]. These are not always available, but it is worth a quick web search to find out.

Identify Author Roles Early in the Process

Early in the writing process, identify authors, determine the order of authors, and discuss the responsibilities of each author. Standard author responsibilities have been identified by The International Committee of Medical Journal Editors (ICMJE) [ 2 ]. To set clear expectations about each team member’s responsibilities and prevent errors in communication, we also suggest outlining more detailed roles, such as who will draft each section of the manuscript, write the abstract, submit the paper electronically, serve as corresponding author, and write the cover letter. It is best to formalize this agreement in writing after discussing it, circulating the document to the author team for approval. We suggest creating a title page on which all authors are listed in the agreed-upon order. It may be necessary to adjust authorship roles and order during the development of the paper. If a new author order is agreed upon, be sure to update the title page in the manuscript draft.

In the case where multiple papers will result from a single study, authors should discuss who will author each paper. Additionally, authors should agree on a deadline for each paper and the lead author should take responsibility for producing an initial draft by this deadline.

Structure of the Introduction Section

The introduction section should be approximately three to five paragraphs in length. Look at examples from your target journal to decide the appropriate length. This section should include the elements shown in Fig.  1 . Begin with a general context, narrowing to the specific focus of the paper. Include five main elements: why your research is important, what is already known about the topic, the “gap” or what is not yet known about the topic, why it is important to learn the new information that your research adds, and the specific research aim(s) that your paper addresses. Your research aim should address the gap you identified. Be sure to add enough background information to enable readers to understand your study. Table 1 provides common introduction section pitfalls and recommendations for addressing them.

The main elements of the introduction section of an original research article. Often, the elements overlap

Methods Section

The purpose of the methods section is twofold: to explain how the study was done in enough detail to enable its replication and to provide enough contextual detail to enable readers to understand and interpret the results. In general, the essential elements of a methods section are the following: a description of the setting and participants, the study design and timing, the recruitment and sampling, the data collection process, the dataset, the dependent and independent variables, the covariates, the analytic approach for each research objective, and the ethical approval. The hallmark of an exemplary methods section is the justification of why each method was used. Table 2 provides common methods section pitfalls and recommendations for addressing them.

Results Section

The focus of the results section should be associations, or lack thereof, rather than statistical tests. Two considerations should guide your writing here. First, the results should present answers to each part of the research aim. Second, return to the methods section to ensure that the analysis and variables for each result have been explained.

Begin the results section by describing the number of participants in the final sample and details such as the number who were approached to participate, the proportion who were eligible and who enrolled, and the number of participants who dropped out. The next part of the results should describe the participant characteristics. After that, you may organize your results by the aim or by putting the most exciting results first. Do not forget to report your non-significant associations. These are still findings.

Tables and figures capture the reader’s attention and efficiently communicate your main findings [ 3 ]. Each table and figure should have a clear message and should complement, rather than repeat, the text. Tables and figures should communicate all salient details necessary for a reader to understand the findings without consulting the text. Include information on comparisons and tests, as well as information about the sample and timing of the study in the title, legend, or in a footnote. Note that figures are often more visually interesting than tables, so if it is feasible to make a figure, make a figure. To avoid confusing the reader, either avoid abbreviations in tables and figures, or define them in a footnote. Note that there should not be citations in the results section and you should not interpret results here. Table 3 provides common results section pitfalls and recommendations for addressing them.

Discussion Section

Opposite the introduction section, the discussion should take the form of a right-side-up triangle beginning with interpretation of your results and moving to general implications (Fig.  2 ). This section typically begins with a restatement of the main findings, which can usually be accomplished with a few carefully-crafted sentences.

Major elements of the discussion section of an original research article. Often, the elements overlap

Next, interpret the meaning or explain the significance of your results, lifting the reader’s gaze from the study’s specific findings to more general applications. Then, compare these study findings with other research. Are these findings in agreement or disagreement with those from other studies? Does this study impart additional nuance to well-accepted theories? Situate your findings within the broader context of scientific literature, then explain the pathways or mechanisms that might give rise to, or explain, the results.

Journals vary in their approach to strengths and limitations sections: some are embedded paragraphs within the discussion section, while some mandate separate section headings. Keep in mind that every study has strengths and limitations. Candidly reporting yours helps readers to correctly interpret your research findings.

The next element of the discussion is a summary of the potential impacts and applications of the research. Should these results be used to optimally design an intervention? Does the work have implications for clinical protocols or public policy? These considerations will help the reader to further grasp the possible impacts of the presented work.

Finally, the discussion should conclude with specific suggestions for future work. Here, you have an opportunity to illuminate specific gaps in the literature that compel further study. Avoid the phrase “future research is necessary” because the recommendation is too general to be helpful to readers. Instead, provide substantive and specific recommendations for future studies. Table 4 provides common discussion section pitfalls and recommendations for addressing them.

Follow the Journal’s Author Guidelines

After you select a target journal, identify the journal’s author guidelines to guide the formatting of your manuscript and references. Author guidelines will often (but not always) include instructions for titles, cover letters, and other components of a manuscript submission. Read the guidelines carefully. If you do not follow the guidelines, your article will be sent back to you.

Finally, do not submit your paper to more than one journal at a time. Even if this is not explicitly stated in the author guidelines of your target journal, it is considered inappropriate and unprofessional.

Your title should invite readers to continue reading beyond the first page [ 4 , 5 ]. It should be informative and interesting. Consider describing the independent and dependent variables, the population and setting, the study design, the timing, and even the main result in your title. Because the focus of the paper can change as you write and revise, we recommend you wait until you have finished writing your paper before composing the title.

Be sure that the title is useful for potential readers searching for your topic. The keywords you select should complement those in your title to maximize the likelihood that a researcher will find your paper through a database search. Avoid using abbreviations in your title unless they are very well known, such as SNP, because it is more likely that someone will use a complete word rather than an abbreviation as a search term to help readers find your paper.

After you have written a complete draft, use the checklist (Fig. 3 ) below to guide your revisions and editing. Additional resources are available on writing the abstract and citing references [ 5 ]. When you feel that your work is ready, ask a trusted colleague or two to read the work and provide informal feedback. The box below provides a checklist that summarizes the key points offered in this article.

Checklist for manuscript quality

Data Availability

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International Committee of Medical Journal Editors. Defining the role of authors and contributors: who is an author? http://www.icmje.org/recommendations/browse/roles-and-responsibilities/defining-the-role-of-authosrs-and-contributors.html . Accessed 15 January, 2020

Vetto JT (2014) Short and sweet: a short course on concise medical writing. J Cancer Educ 29(1):194–195

Brett M, Kording K (2017) Ten simple rules for structuring papers. PLoS ComputBiol. https://doi.org/10.1371/journal.pcbi.1005619

Lang TA (2017) Writing a better research article. J Public Health Emerg. https://doi.org/10.21037/jphe.2017.11.06

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Acknowledgments

Ella August is grateful to the Sustainable Sciences Institute for mentoring her in training researchers on writing and publishing their research.

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Busse, C., August, E. How to Write and Publish a Research Paper for a Peer-Reviewed Journal. J Canc Educ 36 , 909–913 (2021). https://doi.org/10.1007/s13187-020-01751-z

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Published : 30 April 2020

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DOI : https://doi.org/10.1007/s13187-020-01751-z

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• Tourism, Leisure and Hospitality Management
• Transplantation
• Transportation
• Urban Studies
• Veterinary (miscellaneous)
• Visual Arts and Performing Arts
• Waste Management and Disposal
• Water Science and Technology
• All regions / countries
• Asiatic Region
• Eastern Europe
• Latin America
• Middle East
• Northern America
• Pacific Region
• Western Europe
• ARAB COUNTRIES
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• Citable Docs. (3years)
• Total Cites (3years)

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	Title	Type
1		journal	106.094 Q1	211	49	124	4844	35427	89	381.89	98.86	43.95
2		journal	37.044 Q1	39	3	13	897	955	13	100.11	299.00	27.78
3		journal	35.910 Q1	508	123	336	11462	13599	153	34.50	93.19	29.41
4		journal	30.448 Q1	306	47	136	3645	2240	136	11.14	77.55	26.67
5		journal	26.837 Q1	505	105	304	10805	10951	163	31.23	102.90	44.33
6		journal	24.342 Q1	892	439	1496	32820	53447	1207	31.30	74.76	40.19
7		journal	22.399 Q1	391	239	731	8584	13091	153	19.72	35.92	34.15
8		journal	22.344 Q1	359	95	353	6242	4811	351	10.33	65.71	23.89
9		journal	21.836 Q1	184	117	335	8842	13775	196	31.17	75.57	26.86
10		journal	21.048 Q1	217	127	400	9888	10807	183	28.36	77.86	38.85
11		journal	20.544 Q1	1184	1388	4522	14603	107246	1824	21.69	10.52	38.26
12		journal	19.139 Q1	352	83	227	5043	1938	221	7.00	60.76	16.91
13		journal	19.045 Q1	630	595	1363	16478	36243	729	27.23	27.69	43.99
14		journal	18.663 Q1	71	0	19	0	963	19	0.00	0.00	0.00
15		journal	18.587 Q1	23	11	16	0	802	16	47.57	0.00	81.69
16		journal	18.530 Q1	215	83	261	4493	2531	258	7.04	54.13	17.80
17		journal	18.509 Q1	1391	3770	8037	74917	160102	3840	19.40	19.87	38.12
18		journal	18.117 Q1	511	485	1066	13393	17008	461	13.24	27.61	35.19
19		journal	17.828 Q1	833	271	851	115878	50519	819	49.76	427.59	30.50
20		journal	17.701 Q1	223	75	273	3371	1946	268	6.24	44.95	13.84
21		journal	17.654 Q1	234	108	410	6448	4495	409	8.04	59.70	16.43
22		journal	17.507 Q1	398	178	590	11546	12604	360	19.83	64.87	41.91
23		journal	17.497 Q1	229	229	609	6629	16808	379	26.18	28.95	29.53
24		journal	17.300 Q1	639	336	654	13672	13100	504	19.88	40.69	37.01
25		journal	16.061 Q1	388	36	103	14097	4303	99	42.66	391.58	14.94
26		journal	16.009 Q1	467	169	540	11148	13815	304	23.17	65.96	36.44
27		journal	15.966 Q1	264	102	252	19168	11266	244	38.64	187.92	24.30
28		journal	15.827 Q1	140	106	297	4359	4041	62	12.99	41.12	41.35
29		journal	15.620 Q1	328	23	84	4178	2696	83	27.02	181.65	40.68
30		journal	14.943 Q1	115	16	42	403	896	41	24.10	25.19	77.78
31		journal	14.796 Q1	388	400	978	11477	15900	588	17.52	28.69	33.83
32		journal	14.780 Q1	123	0	13	0	374	11	12.56	0.00	0.00
33		journal	14.707 Q1	32	46	35	4815	2160	34	61.71	104.67	36.44
34		journal	14.618 Q1	160	70	247	587	5353	230	21.11	8.39	58.79
35		journal	14.605 Q1	109	23	72	5797	1938	70	14.90	252.04	45.57
36		journal	14.577 Q1	419	262	637	10044	17562	466	27.42	38.34	28.93
37		journal	14.293 Q1	421	123	346	10202	6211	207	17.40	82.94	32.86
38		journal	14.231 Q1	558	306	834	9499	20730	593	24.08	31.04	24.85
39		journal	14.175 Q1	210	28	92	3163	1260	86	10.59	112.96	42.59
40		journal	13.942 Q1	294	144	670	5180	12698	362	18.81	35.97	39.02
41		book series	13.670 Q1	210	14	42	3772	1271	39	23.96	269.43	26.09
42		journal	13.655 Q1	311	89	563	4857	6315	559	11.14	54.57	23.11
43		journal	13.609 Q1	165	93	250	5332	1699	250	6.02	57.33	15.88
44		journal	13.578 Q1	455	233	688	15608	13409	550	16.89	66.99	40.35
45		journal	13.315 Q1	136	180	471	6682	12109	368	24.27	37.12	26.28
46		journal	13.080 Q1	260	243	827	1865	14374	679	16.58	7.67	62.53
47		journal	12.511 Q1	635	252	983	61439	44032	979	38.71	243.81	32.40
48		journal	12.324 Q1	81	55	137	2838	862	137	6.20	51.60	17.36
49		journal	12.294 Q1	46	62	154	8174	4174	86	27.10	131.84	31.14
50		journal	12.288 Q1	44	60	79	4858	3323	78	42.06	80.97	33.06

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NASA Discovers a Long-Sought Global Electric Field on Earth

• A rocket team reports the first successful detection of Earth’s ambipolar electric field: a weak, planet-wide electric field as fundamental as Earth’s gravity and magnetic fields.
• First hypothesized more than 60 years ago, the ambipolar electric field is a key driver of the “polar wind,” a steady outflow of charged particles into space that occurs above Earth’s poles.
• This electric field lifts charged particles in our upper atmosphere to greater heights than they would otherwise reach and may have shaped our planet’s evolution in ways yet to be explored.

Using observations from a NASA suborbital rocket, an international team of scientists has, for the first time, successfully measured a planet-wide electric field thought to be as fundamental to Earth as its gravity and magnetic fields. Known as the ambipolar electric field, scientists first hypothesized over 60 years ago that it drove how our planet’s atmosphere can escape above Earth’s North and South Poles. Measurements from the rocket, NASA’s Endurance mission , have confirmed the existence of the ambipolar field and quantified its strength, revealing its role in driving atmospheric escape and shaping our ionosphere — a layer of the upper atmosphere — more broadly.

Understanding the complex movements and evolution of our planet’s atmosphere provides clues not only to the history of Earth but also gives us insight into the mysteries of other planets and determining which ones might be hospitable to life. The paper was published Wednesday, Aug. 28, 2024, in the journal Nature .

An Electric Field Drawing Particles Out to Space

Since the late 1960s, spacecraft flying over Earth’s poles have detected a stream of particles flowing from our atmosphere into space. Theorists predicted this outflow, which they dubbed the “polar wind,” spurring research to understand its causes. 

Some amount of outflow from our atmosphere was expected. Intense, unfiltered sunlight should cause some particles from our air to escape into space, like steam evaporating from a pot of water. But the observed polar wind was more mysterious. Many particles within it were cold, with no signs they had been heated — yet they were traveling at supersonic speeds.

“Something had to be drawing these particles out of the atmosphere,” said Glyn Collinson, principal investigator of Endurance at NASA’s Goddard Space Flight Center in Greenbelt, Maryland, and lead author of the paper. Scientists suspected a yet-to-be-discovered electric field could be at work.

The hypothesized electric field, generated at the subatomic scale, was expected to be incredibly weak, with its effects felt only over hundreds of miles. For decades, detecting it was beyond the limits of existing technology. In 2016, Collinson and his team got to work inventing a new instrument they thought was up to the task of measuring Earth’s ambipolar field.

How the Ambipolar Field Works

A weak electric field in the upper atmosphere may loft charged particles into space..

Scientists theorized this electric field should begin at around 150 miles (250 kilometers) altitude, where atoms in our atmosphere break apart into negatively charged electrons and positively charged ions. Electrons are incredibly light — the slightest kick of energy could send them shooting out to space. Ions are at least 1,836 times heavier and tend to sink toward the ground. If gravity alone were in play, the two populations, once separated, would drift apart over time. But given their opposite electric charges, an electric field forms to tether them together, preventing any separation of charges and counteracting some of the effects of gravity.

This electric field is bidirectional, or “ambipolar,” because it works in both directions. Ions pull the electrons down with them as they sink with gravity. At the same time, electrons lift ions to greater heights as they attempt to escape to space, like a tiny dog tugging on its sluggish owner’s leash. The net effect of the ambipolar field is to extend the height of the atmosphere, lifting some ions high enough to escape with the polar wind. Animation credits: NASA/Conceptual Image Lab/Wes Buchanan/Krystofer Kim

Launching a Rocket from the Arctic

The team’s instruments and ideas were best suited for a suborbital rocket flight launched from the Arctic. In a nod to the ship that carried Ernest Shackleton on his famous 1914 voyage to Antarctica, the team named their mission Endurance. The scientists set a course for Svalbard, a Norwegian archipelago just a few hundred miles from the North Pole and home to the northernmost rocket range in the world.

“Svalbard is the only rocket range in the world where you can fly through the polar wind and make the measurements we needed,” said Suzie Imber, a space physicist at the University of Leicester, UK, and co-author of the paper.

On May 11, 2022, Endurance launched and reached an altitude of 477.23 miles (768.03 kilometers), splashing down 19 minutes later in the Greenland Sea. Across the 322-mile altitude range where it collected data, Endurance measured a change in electric potential of only 0.55 volts.

“A half a volt is almost nothing — it’s only about as strong as a watch battery,” Collinson said. “But that’s just the right amount to explain the polar wind.”

Hydrogen ions, the most abundant type of particle in the polar wind, experience an outward force from this field 10.6 times stronger than gravity. “That’s more than enough to counter gravity — in fact, it’s enough to launch them upwards into space at supersonic speeds,” said Alex Glocer, Endurance project scientist at NASA Goddard and co-author of the paper.

Heavier particles also get a boost. Oxygen ions at that same altitude, immersed in this half-a-volt field, weigh half as much. In general, the team found that the ambipolar field increases what’s known as the “scale height” of the ionosphere by 271%, meaning the ionosphere remains denser to greater heights than it would be without it.

“It’s like this conveyor belt, lifting the atmosphere up into space,” Collinson added.

Endurance’s discovery has opened many new paths for exploration. The ambipolar field, as a fundamental energy field of our planet alongside gravity and magnetism, may have continuously shaped the evolution of our atmosphere in ways we can now begin to explore. Because it’s created by the internal dynamics of an atmosphere, similar electric fields are expected to exist on other planets, including Venus and Mars.

“Any planet with an atmosphere should have an ambipolar field,” Collinson said. “Now that we’ve finally measured it, we can begin learning how it’s shaped our planet as well as others over time.”

By Miles Hatfield and Rachel Lense NASA’s Goddard Space Flight Center, Greenbelt, Md. Media Contact: Sarah Frazier, [email protected]

Endurance was a NASA-funded mission conducted through the Sounding Rocket Program at NASA’s Wallops Flight Facility in Virginia. The Svalbard Rocket Range is owned and operated by Andøya Space. The European Incoherent Scatter Scientific Association (EISCAT) Svalbard radar, located in Longyearbyen, made ground-based measurements of the ionosphere critical to interpreting the rocket data. The United Kingdom Natural Environment Research Council (NERC) and the Research Council of Norway (RCN) funded the EISCAT radar for the Endurance mission. EISCAT is owned and operated by research institutes and research councils of Norway, Sweden, Finland, Japan, China, and the United Kingdom (the EISCAT Associates). The Endurance mission team encompasses affiliates of the Catholic University of America, Embry-Riddle Aeronautical University, the University of California, Berkeley, the University of Colorado at Boulder, the University of Leicester, U.K., the University of New Hampshire, and Penn State University.

Related Terms

• Goddard Space Flight Center
• Heliophysics
• Heliophysics Division
• Science & Research
• Sounding Rockets
• Sounding Rockets Program

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GPT-fabricated scientific papers on Google Scholar: Key features, spread, and implications for preempting evidence manipulation

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Academic journals, archives, and repositories are seeing an increasing number of questionable research papers clearly produced using generative AI. They are often created with widely available, general-purpose AI applications, most likely ChatGPT, and mimic scientific writing. Google Scholar easily locates and lists these questionable papers alongside reputable, quality-controlled research. Our analysis of a selection of questionable GPT-fabricated scientific papers found in Google Scholar shows that many are about applied, often controversial topics susceptible to disinformation: the environment, health, and computing. The resulting enhanced potential for malicious manipulation of society’s evidence base, particularly in politically divisive domains, is a growing concern.

Swedish School of Library and Information Science, University of Borås, Sweden

Department of Arts and Cultural Sciences, Lund University, Sweden

Division of Environmental Communication, Swedish University of Agricultural Sciences, Sweden

Research Questions

• Where are questionable publications produced with generative pre-trained transformers (GPTs) that can be found via Google Scholar published or deposited?
• What are the main characteristics of these publications in relation to predominant subject categories?
• How are these publications spread in the research infrastructure for scholarly communication?
• How is the role of the scholarly communication infrastructure challenged in maintaining public trust in science and evidence through inappropriate use of generative AI?

research note Summary

• A sample of scientific papers with signs of GPT-use found on Google Scholar was retrieved, downloaded, and analyzed using a combination of qualitative coding and descriptive statistics. All papers contained at least one of two common phrases returned by conversational agents that use large language models (LLM) like OpenAI’s ChatGPT. Google Search was then used to determine the extent to which copies of questionable, GPT-fabricated papers were available in various repositories, archives, citation databases, and social media platforms.
• Roughly two-thirds of the retrieved papers were found to have been produced, at least in part, through undisclosed, potentially deceptive use of GPT. The majority (57%) of these questionable papers dealt with policy-relevant subjects (i.e., environment, health, computing), susceptible to influence operations. Most were available in several copies on different domains (e.g., social media, archives, and repositories).
• Two main risks arise from the increasingly common use of GPT to (mass-)produce fake, scientific publications. First, the abundance of fabricated “studies” seeping into all areas of the research infrastructure threatens to overwhelm the scholarly communication system and jeopardize the integrity of the scientific record. A second risk lies in the increased possibility that convincingly scientific-looking content was in fact deceitfully created with AI tools and is also optimized to be retrieved by publicly available academic search engines, particularly Google Scholar. However small, this possibility and awareness of it risks undermining the basis for trust in scientific knowledge and poses serious societal risks.

Implications

The use of ChatGPT to generate text for academic papers has raised concerns about research integrity. Discussion of this phenomenon is ongoing in editorials, commentaries, opinion pieces, and on social media (Bom, 2023; Stokel-Walker, 2024; Thorp, 2023). There are now several lists of papers suspected of GPT misuse, and new papers are constantly being added. 1 See for example Academ-AI, https://www.academ-ai.info/ , and Retraction Watch, https://retractionwatch.com/papers-and-peer-reviews-with-evidence-of-chatgpt-writing/ . While many legitimate uses of GPT for research and academic writing exist (Huang & Tan, 2023; Kitamura, 2023; Lund et al., 2023), its undeclared use—beyond proofreading—has potentially far-reaching implications for both science and society, but especially for their relationship. It, therefore, seems important to extend the discussion to one of the most accessible and well-known intermediaries between science, but also certain types of misinformation, and the public, namely Google Scholar, also in response to the legitimate concerns that the discussion of generative AI and misinformation needs to be more nuanced and empirically substantiated  (Simon et al., 2023).

Google Scholar, https://scholar.google.com , is an easy-to-use academic search engine. It is available for free, and its index is extensive (Gusenbauer & Haddaway, 2020). It is also often touted as a credible source for academic literature and even recommended in library guides, by media and information literacy initiatives, and fact checkers (Tripodi et al., 2023). However, Google Scholar lacks the transparency and adherence to standards that usually characterize citation databases. Instead, Google Scholar uses automated crawlers, like Google’s web search engine (Martín-Martín et al., 2021), and the inclusion criteria are based on primarily technical standards, allowing any individual author—with or without scientific affiliation—to upload papers to be indexed (Google Scholar Help, n.d.). It has been shown that Google Scholar is susceptible to manipulation through citation exploits (Antkare, 2020) and by providing access to fake scientific papers (Dadkhah et al., 2017). A large part of Google Scholar’s index consists of publications from established scientific journals or other forms of quality-controlled, scholarly literature. However, the index also contains a large amount of gray literature, including student papers, working papers, reports, preprint servers, and academic networking sites, as well as material from so-called “questionable” academic journals, including paper mills. The search interface does not offer the possibility to filter the results meaningfully by material type, publication status, or form of quality control, such as limiting the search to peer-reviewed material.

To understand the occurrence of ChatGPT (co-)authored work in Google Scholar’s index, we scraped it for publications, including one of two common ChatGPT responses (see Appendix A) that we encountered on social media and in media reports (DeGeurin, 2024). The results of our descriptive statistical analyses showed that around 62% did not declare the use of GPTs. Most of these GPT-fabricated papers were found in non-indexed journals and working papers, but some cases included research published in mainstream scientific journals and conference proceedings. 2 Indexed journals mean scholarly journals indexed by abstract and citation databases such as Scopus and Web of Science, where the indexation implies journals with high scientific quality. Non-indexed journals are journals that fall outside of this indexation. More than half (57%) of these GPT-fabricated papers concerned policy-relevant subject areas susceptible to influence operations. To avoid increasing the visibility of these publications, we abstained from referencing them in this research note. However, we have made the data available in the Harvard Dataverse repository.

The publications were related to three issue areas—health (14.5%), environment (19.5%) and computing (23%)—with key terms such “healthcare,” “COVID-19,” or “infection”for health-related papers, and “analysis,” “sustainable,” and “global” for environment-related papers. In several cases, the papers had titles that strung together general keywords and buzzwords, thus alluding to very broad and current research. These terms included “biology,” “telehealth,” “climate policy,” “diversity,” and “disrupting,” to name just a few.  While the study’s scope and design did not include a detailed analysis of which parts of the articles included fabricated text, our dataset did contain the surrounding sentences for each occurrence of the suspicious phrases that formed the basis for our search and subsequent selection. Based on that, we can say that the phrases occurred in most sections typically found in scientific publications, including the literature review, methods, conceptual and theoretical frameworks, background, motivation or societal relevance, and even discussion. This was confirmed during the joint coding, where we read and discussed all articles. It became clear that not just the text related to the telltale phrases was created by GPT, but that almost all articles in our sample of questionable articles likely contained traces of GPT-fabricated text everywhere.

Evidence hacking and backfiring effects

Generative pre-trained transformers (GPTs) can be used to produce texts that mimic scientific writing. These texts, when made available online—as we demonstrate—leak into the databases of academic search engines and other parts of the research infrastructure for scholarly communication. This development exacerbates problems that were already present with less sophisticated text generators (Antkare, 2020; Cabanac & Labbé, 2021). Yet, the public release of ChatGPT in 2022, together with the way Google Scholar works, has increased the likelihood of lay people (e.g., media, politicians, patients, students) coming across questionable (or even entirely GPT-fabricated) papers and other problematic research findings. Previous research has emphasized that the ability to determine the value and status of scientific publications for lay people is at stake when misleading articles are passed off as reputable (Haider & Åström, 2017) and that systematic literature reviews risk being compromised (Dadkhah et al., 2017). It has also been highlighted that Google Scholar, in particular, can be and has been exploited for manipulating the evidence base for politically charged issues and to fuel conspiracy narratives (Tripodi et al., 2023). Both concerns are likely to be magnified in the future, increasing the risk of what we suggest calling evidence hacking —the strategic and coordinated malicious manipulation of society’s evidence base.

The authority of quality-controlled research as evidence to support legislation, policy, politics, and other forms of decision-making is undermined by the presence of undeclared GPT-fabricated content in publications professing to be scientific. Due to the large number of archives, repositories, mirror sites, and shadow libraries to which they spread, there is a clear risk that GPT-fabricated, questionable papers will reach audiences even after a possible retraction. There are considerable technical difficulties involved in identifying and tracing computer-fabricated papers (Cabanac & Labbé, 2021; Dadkhah et al., 2023; Jones, 2024), not to mention preventing and curbing their spread and uptake.

However, as the rise of the so-called anti-vaxx movement during the COVID-19 pandemic and the ongoing obstruction and denial of climate change show, retracting erroneous publications often fuels conspiracies and increases the following of these movements rather than stopping them. To illustrate this mechanism, climate deniers frequently question established scientific consensus by pointing to other, supposedly scientific, studies that support their claims. Usually, these are poorly executed, not peer-reviewed, based on obsolete data, or even fraudulent (Dunlap & Brulle, 2020). A similar strategy is successful in the alternative epistemic world of the global anti-vaccination movement (Carrion, 2018) and the persistence of flawed and questionable publications in the scientific record already poses significant problems for health research, policy, and lawmakers, and thus for society as a whole (Littell et al., 2024). Considering that a person’s support for “doing your own research” is associated with increased mistrust in scientific institutions (Chinn & Hasell, 2023), it will be of utmost importance to anticipate and consider such backfiring effects already when designing a technical solution, when suggesting industry or legal regulation, and in the planning of educational measures.

Recommendations

Solutions should be based on simultaneous considerations of technical, educational, and regulatory approaches, as well as incentives, including social ones, across the entire research infrastructure. Paying attention to how these approaches and incentives relate to each other can help identify points and mechanisms for disruption. Recognizing fraudulent academic papers must happen alongside understanding how they reach their audiences and what reasons there might be for some of these papers successfully “sticking around.” A possible way to mitigate some of the risks associated with GPT-fabricated scholarly texts finding their way into academic search engine results would be to provide filtering options for facets such as indexed journals, gray literature, peer-review, and similar on the interface of publicly available academic search engines. Furthermore, evaluation tools for indexed journals 3 Such as LiU Journal CheckUp, https://ep.liu.se/JournalCheckup/default.aspx?lang=eng . could be integrated into the graphical user interfaces and the crawlers of these academic search engines. To enable accountability, it is important that the index (database) of such a search engine is populated according to criteria that are transparent, open to scrutiny, and appropriate to the workings of  science and other forms of academic research. Moreover, considering that Google Scholar has no real competitor, there is a strong case for establishing a freely accessible, non-specialized academic search engine that is not run for commercial reasons but for reasons of public interest. Such measures, together with educational initiatives aimed particularly at policymakers, science communicators, journalists, and other media workers, will be crucial to reducing the possibilities for and effects of malicious manipulation or evidence hacking. It is important not to present this as a technical problem that exists only because of AI text generators but to relate it to the wider concerns in which it is embedded. These range from a largely dysfunctional scholarly publishing system (Haider & Åström, 2017) and academia’s “publish or perish” paradigm to Google’s near-monopoly and ideological battles over the control of information and ultimately knowledge. Any intervention is likely to have systemic effects; these effects need to be considered and assessed in advance and, ideally, followed up on.

Our study focused on a selection of papers that were easily recognizable as fraudulent. We used this relatively small sample as a magnifying glass to examine, delineate, and understand a problem that goes beyond the scope of the sample itself, which however points towards larger concerns that require further investigation. The work of ongoing whistleblowing initiatives 4 Such as Academ-AI, https://www.academ-ai.info/ , and Retraction Watch, https://retractionwatch.com/papers-and-peer-reviews-with-evidence-of-chatgpt-writing/ . , recent media reports of journal closures (Subbaraman, 2024), or GPT-related changes in word use and writing style (Cabanac et al., 2021; Stokel-Walker, 2024) suggest that we only see the tip of the iceberg. There are already more sophisticated cases (Dadkhah et al., 2023) as well as cases involving fabricated images (Gu et al., 2022). Our analysis shows that questionable and potentially manipulative GPT-fabricated papers permeate the research infrastructure and are likely to become a widespread phenomenon. Our findings underline that the risk of fake scientific papers being used to maliciously manipulate evidence (see Dadkhah et al., 2017) must be taken seriously. Manipulation may involve undeclared automatic summaries of texts, inclusion in literature reviews, explicit scientific claims, or the concealment of errors in studies so that they are difficult to detect in peer review. However, the mere possibility of these things happening is a significant risk in its own right that can be strategically exploited and will have ramifications for trust in and perception of science. Society’s methods of evaluating sources and the foundations of media and information literacy are under threat and public trust in science is at risk of further erosion, with far-reaching consequences for society in dealing with information disorders. To address this multifaceted problem, we first need to understand why it exists and proliferates.

Finding 1: 139 GPT-fabricated, questionable papers were found and listed as regular results on the Google Scholar results page. Non-indexed journals dominate.

Most questionable papers we found were in non-indexed journals or were working papers, but we did also find some in established journals, publications, conferences, and repositories. We found a total of 139 papers with a suspected deceptive use of ChatGPT or similar LLM applications (see Table 1). Out of these, 19 were in indexed journals, 89 were in non-indexed journals, 19 were student papers found in university databases, and 12 were working papers (mostly in preprint databases). Table 1 divides these papers into categories. Health and environment papers made up around 34% (47) of the sample. Of these, 66% were present in non-indexed journals.

Indexed journals*	5	3	4	7	19
Non-indexed journals	18	18	13	40	89
Student papers	4	3	1	11	19
Working papers	5	3	2	2	12
Total	32	27	20	60	139

Finding 2: GPT-fabricated, questionable papers are disseminated online, permeating the research infrastructure for scholarly communication, often in multiple copies. Applied topics with practical implications dominate.

The 20 papers concerning health-related issues are distributed across 20 unique domains, accounting for 46 URLs. The 27 papers dealing with environmental issues can be found across 26 unique domains, accounting for 56 URLs.  Most of the identified papers exist in multiple copies and have already spread to several archives, repositories, and social media. It would be difficult, or impossible, to remove them from the scientific record.

As apparent from Table 2, GPT-fabricated, questionable papers are seeping into most parts of the online research infrastructure for scholarly communication. Platforms on which identified papers have appeared include ResearchGate, ORCiD, Journal of Population Therapeutics and Clinical Pharmacology (JPTCP), Easychair, Frontiers, the Institute of Electrical and Electronics Engineer (IEEE), and X/Twitter. Thus, even if they are retracted from their original source, it will prove very difficult to track, remove, or even just mark them up on other platforms. Moreover, unless regulated, Google Scholar will enable their continued and most likely unlabeled discoverability.

Environment	researchgate.net (13)	orcid.org (4)	easychair.org (3)	ijope.com* (3)	publikasiindonesia.id (3)
Health	researchgate.net (15)	ieee.org (4)	twitter.com (3)	jptcp.com** (2)	frontiersin.org (2)

A word rain visualization (Centre for Digital Humanities Uppsala, 2023), which combines word prominences through TF-IDF 5 Term frequency–inverse document frequency , a method for measuring the significance of a word in a document compared to its frequency across all documents in a collection. scores with semantic similarity of the full texts of our sample of GPT-generated articles that fall into the “Environment” and “Health” categories, reflects the two categories in question. However, as can be seen in Figure 1, it also reveals overlap and sub-areas. The y-axis shows word prominences through word positions and font sizes, while the x-axis indicates semantic similarity. In addition to a certain amount of overlap, this reveals sub-areas, which are best described as two distinct events within the word rain. The event on the left bundles terms related to the development and management of health and healthcare with “challenges,” “impact,” and “potential of artificial intelligence”emerging as semantically related terms. Terms related to research infrastructures, environmental, epistemic, and technological concepts are arranged further down in the same event (e.g., “system,” “climate,” “understanding,” “knowledge,” “learning,” “education,” “sustainable”). A second distinct event further to the right bundles terms associated with fish farming and aquatic medicinal plants, highlighting the presence of an aquaculture cluster.  Here, the prominence of groups of terms such as “used,” “model,” “-based,” and “traditional” suggests the presence of applied research on these topics. The two events making up the word rain visualization, are linked by a less dominant but overlapping cluster of terms related to “energy” and “water.”

The bar chart of the terms in the paper subset (see Figure 2) complements the word rain visualization by depicting the most prominent terms in the full texts along the y-axis. Here, word prominences across health and environment papers are arranged descendingly, where values outside parentheses are TF-IDF values (relative frequencies) and values inside parentheses are raw term frequencies (absolute frequencies).

Finding 3: Google Scholar presents results from quality-controlled and non-controlled citation databases on the same interface, providing unfiltered access to GPT-fabricated questionable papers.

Google Scholar’s central position in the publicly accessible scholarly communication infrastructure, as well as its lack of standards, transparency, and accountability in terms of inclusion criteria, has potentially serious implications for public trust in science. This is likely to exacerbate the already-known potential to exploit Google Scholar for evidence hacking (Tripodi et al., 2023) and will have implications for any attempts to retract or remove fraudulent papers from their original publication venues. Any solution must consider the entirety of the research infrastructure for scholarly communication and the interplay of different actors, interests, and incentives.

We searched and scraped Google Scholar using the Python library Scholarly (Cholewiak et al., 2023) for papers that included specific phrases known to be common responses from ChatGPT and similar applications with the same underlying model (GPT3.5 or GPT4): “as of my last knowledge update” and/or “I don’t have access to real-time data” (see Appendix A). This facilitated the identification of papers that likely used generative AI to produce text, resulting in 227 retrieved papers. The papers’ bibliographic information was automatically added to a spreadsheet and downloaded into Zotero. 6 An open-source reference manager, https://zotero.org .

We employed multiple coding (Barbour, 2001) to classify the papers based on their content. First, we jointly assessed whether the paper was suspected of fraudulent use of ChatGPT (or similar) based on how the text was integrated into the papers and whether the paper was presented as original research output or the AI tool’s role was acknowledged. Second, in analyzing the content of the papers, we continued the multiple coding by classifying the fraudulent papers into four categories identified during an initial round of analysis—health, environment, computing, and others—and then determining which subjects were most affected by this issue (see Table 1). Out of the 227 retrieved papers, 88 papers were written with legitimate and/or declared use of GPTs (i.e., false positives, which were excluded from further analysis), and 139 papers were written with undeclared and/or fraudulent use (i.e., true positives, which were included in further analysis). The multiple coding was conducted jointly by all authors of the present article, who collaboratively coded and cross-checked each other’s interpretation of the data simultaneously in a shared spreadsheet file. This was done to single out coding discrepancies and settle coding disagreements, which in turn ensured methodological thoroughness and analytical consensus (see Barbour, 2001). Redoing the category coding later based on our established coding schedule, we achieved an intercoder reliability (Cohen’s kappa) of 0.806 after eradicating obvious differences.

The ranking algorithm of Google Scholar prioritizes highly cited and older publications (Martín-Martín et al., 2016). Therefore, the position of the articles on the search engine results pages was not particularly informative, considering the relatively small number of results in combination with the recency of the publications. Only the query “as of my last knowledge update” had more than two search engine result pages. On those, questionable articles with undeclared use of GPTs were evenly distributed across all result pages (min: 4, max: 9, mode: 8), with the proportion of undeclared use being slightly higher on average on later search result pages.

To understand how the papers making fraudulent use of generative AI were disseminated online, we programmatically searched for the paper titles (with exact string matching) in Google Search from our local IP address (see Appendix B) using the googlesearch – python library(Vikramaditya, 2020). We manually verified each search result to filter out false positives—results that were not related to the paper—and then compiled the most prominent URLs by field. This enabled the identification of other platforms through which the papers had been spread. We did not, however, investigate whether copies had spread into SciHub or other shadow libraries, or if they were referenced in Wikipedia.

We used descriptive statistics to count the prevalence of the number of GPT-fabricated papers across topics and venues and top domains by subject. The pandas software library for the Python programming language (The pandas development team, 2024) was used for this part of the analysis. Based on the multiple coding, paper occurrences were counted in relation to their categories, divided into indexed journals, non-indexed journals, student papers, and working papers. The schemes, subdomains, and subdirectories of the URL strings were filtered out while top-level domains and second-level domains were kept, which led to normalizing domain names. This, in turn, allowed the counting of domain frequencies in the environment and health categories. To distinguish word prominences and meanings in the environment and health-related GPT-fabricated questionable papers, a semantically-aware word cloud visualization was produced through the use of a word rain (Centre for Digital Humanities Uppsala, 2023) for full-text versions of the papers. Font size and y-axis positions indicate word prominences through TF-IDF scores for the environment and health papers (also visualized in a separate bar chart with raw term frequencies in parentheses), and words are positioned along the x-axis to reflect semantic similarity (Skeppstedt et al., 2024), with an English Word2vec skip gram model space (Fares et al., 2017). An English stop word list was used, along with a manually produced list including terms such as “https,” “volume,” or “years.”

• Artificial Intelligence
• / Search engines

Cite this Essay

Haider, J., Söderström, K. R., Ekström, B., & Rödl, M. (2024). GPT-fabricated scientific papers on Google Scholar: Key features, spread, and implications for preempting evidence manipulation. Harvard Kennedy School (HKS) Misinformation Review . https://doi.org/10.37016/mr-2020-156

• / Appendix B

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This research has been supported by Mistra, the Swedish Foundation for Strategic Environmental Research, through the research program Mistra Environmental Communication (Haider, Ekström, Rödl) and the Marcus and Amalia Wallenberg Foundation [2020.0004] (Söderström).

Competing Interests

The authors declare no competing interests.

The research described in this article was carried out under Swedish legislation. According to the relevant EU and Swedish legislation (2003:460) on the ethical review of research involving humans (“Ethical Review Act”), the research reported on here is not subject to authorization by the Swedish Ethical Review Authority (“etikprövningsmyndigheten”) (SRC, 2017).

This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided that the original author and source are properly credited.

Data Availability

All data needed to replicate this study are available at the Harvard Dataverse: https://doi.org/10.7910/DVN/WUVD8X

Acknowledgements

The authors wish to thank two anonymous reviewers for their valuable comments on the article manuscript as well as the editorial group of Harvard Kennedy School (HKS) Misinformation Review for their thoughtful feedback and input.

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Collection  12 March 2023

Journal Top 100 - 2022

This collection highlights our most downloaded* research papers published in 2022. Featuring authors from around the world, these papers highlight valuable research from an international community.

You can also check out the Top 100 across various subject areas here .

*Data obtained from SN Insights, which is based on Digital Science’s Dimensions.

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A gigantic bizarre marine turtle (Testudines: Chelonioidea) from the Middle Campanian (Late Cretaceous) of South-western Europe

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The first experience with fully endoscopic posterior cervical foraminotomy and discectomy for radiculopathy performed in Viet Duc University Hospital

• Son Ngoc Dinh
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Mapping the “catscape” formed by a population of pet cats with outdoor access

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Investigation of humans individual differences as predictors of their animal interaction styles, focused on the domestic cat

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Genesis of fecal floatation is causally linked to gut microbial colonization in mice

• Syed Mohammed Musheer Aalam
• Daphne Norma Crasta
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Young children’s screen time during the first COVID-19 lockdown in 12 countries

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Cichlids and stingrays can add and subtract ‘one’ in the number space from one to five

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Group VR experiences can produce ego attenuation and connectedness comparable to psychedelics

• David R. Glowacki
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New therizinosaurid dinosaur from the marine Osoushinai Formation (Upper Cretaceous, Japan) provides insight for function and evolution of therizinosaur claws

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Smartphone-based ecological momentary assessment reveals mental health benefits of birdlife

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Long-term outcomes of cataract surgery with toric intraocular lens implantation by the type of preoperative astigmatism

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• Published: 09 September 2024

A multi-state evaluation of extreme risk protection orders: a research protocol

• April M. Zeoli 1 ,
• Amy Molocznik 2 ,
• Jennifer Paruk 3 ,
• Elise Omaki 2 ,
• Shannon Frattaroli 2 ,
• Marian E. Betz 4 ,
• Annette Christy 5 ,
• Reena Kapoor 6 ,
• Christopher Knoepke 7 ,
• Wenjuan Ma 8 ,
• Michael A. Norko 6 ,
• Veronica A. Pear 9 ,
• Ali Rowhani-Rahbar 10 ,
• Julia P. Schleimer 9 ,
• Jeffrey W. Swanson 11 &
• Garen J. Wintemute 9  

Injury Epidemiology volume  11 , Article number:  49 ( 2024 ) Cite this article

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Metrics details

Extreme Risk Protection Orders (ERPOs) are civil court orders that prohibit firearm purchase and possession when someone is behaving dangerously and is at risk of harming themselves and/or others. As of June 2024, ERPOs are available in 21 states and the District of Columbia to prevent firearm violence. This paper describes the design and protocol of a six-state study of ERPO use.

The six states included are California, Colorado, Connecticut, Florida, Maryland, and Washington. During the 3-year project period (2020–2023), ERPO case files were obtained through public records requests or through agreements with agencies with access to these data in each state. A team of over four dozen research assistants from seven institutions coded 6628 ERPO cases, abstracting 80 variables per case under domains related to respondent characteristics, events and behaviors leading to ERPO petitions, petitioner types, and court outcomes. Research assistants received didactic training through an online learning management system that included virtual training modules, quizzes, practice coding exercises, and two virtual synchronous sessions. A protocol for gaining strong interrater reliability was used. Research assistants also learned strategies for reducing the risk of experiencing secondary trauma through the coding process, identifying its occurrence, and obtaining help.

Addressing firearm violence in the U.S. is a priority. Understanding ERPO use in these six states can inform implementation planning and ERPO uptake, including promising opportunities to enhance safety and prevent firearm-related injuries and deaths. By publishing this protocol, we offer detailed insight into the methods underlying the papers published from these data, and the process of managing data abstraction from ERPO case files across the multi-state and multi-institution teams involved. Such information may also inform future analyses of this data, and future replication efforts.

Registration

This protocol is registered on Open Science Framework ( https://osf.io/kv4fc/ ).

In 2022 in the United States, over 27,000 people died by firearm suicide and more than 20,000 people were killed as a result of interpersonal firearm violence resulting in 14.2/100,000 people dying from intentional firearm injuries that year (Centers for Disease Control and Prevention, National Center for Injury Prevention and Control 2023 ). Preventing firearm access by those identified to be at risk of harming themselves and/or others is a logical strategy to reduce firearm homicide, firearm suicide, and nonfatal firearm violence. One promising and innovative opportunity to address firearm violence, therefore, is with extreme risk protection order (ERPO) laws. ERPO laws, or “red flag laws” as they are often called in popular discourse, provide a civil court process to temporarily prohibit firearm purchase and possession by individuals who are behaving dangerously and are at risk of harming themselves or others. As of June 2024, 21 states and the District of Columbia have passed ERPO-style bills into law (Johns Hopkins Bloomberg School of Public Health 2024 ). ERPOs fill an important policy gap because some individuals at risk of harming themselves and/or others, are legally able to purchase and possess firearms and cannot otherwise be disarmed. Therefore, ERPOs provide a mechanism for preventing firearm access (and potentially firearm violence) when an individual who represents a credible threat of violence is known but is not prohibited from accessing firearms by other legal mechanisms.

Research on ERPOs and their use and outcomes is in its infancy. Multiple studies have described characteristics of ERPO respondents and risk behaviors detailed in the applications in a single state or county ERPO (Barnard et al. 2021 ; Frattaroli et al. 2020 ; Pear et al. 2022 ; Rowhani-Rahbar et al. 2020 ; Swanson et al. 2019 , 2017 ; Zeoli et al. 2021 ). Few studies have examined outcomes, and those that have generally focus on suicide outcomes, with findings suggestive of a reduction in suicide risk when ERPOs are used (Swanson et al. 2019 , 2017 ; Miller et al. 2024 ) and an association at the state-level between ERPO law enactment and a reduction in firearm suicides (Kivisto and Phalen 2018 ). To our knowledge, this is the first multi-state ERPO study.

Here we describe the protocol we used to conduct a six-state study of ERPO case files designed to characterize ERPO petitions, petitioners and respondents (individual parties in the ERPO petition), court outcomes, and identify whether ERPOs are associated with reductions in suicide across geographically, demographically, and politically diverse states. The protocol described in this manuscript details (1) how we accessed ERPO case files in six states; (2) an explanation of the process of standardizing data from official records across the six states; (3) guidance for training research assistants (RAs) and maintaining consistent data abstraction practices across a multi-state, multi-institution RA team; and (4) strategies for reducing and responding to secondary trauma risk experienced by RAs as a result of reading ERPO narratives, which can include graphic descriptions of violence and crises.

During the 3-year project period (2020–2023), we conducted a multi-state study (Zeoli et al. 2022 ) of ERPO use with data from six states (California, Colorado, Connecticut, Florida, Maryland, and Washington). We selected these states for three reasons. First, all are engaged in efforts to implement ERPOs, and those implementation efforts are either yielding a critical mass of ERPO petitions filed or an informative implementation context. Second, these states are geographically and politically diverse, which may impact implementation and use. Third, we were able to access ERPO case files in the selected states. While ERPO statutes differ in some ways across the six states (Smart et al. 2020 ), all share a general process that involves a petition, court hearing, and court decision about whether to temporarily prohibit the individual named in the order from purchasing and possessing firearms.

ERPO court records are publicly available for all study states except Maryland. In the five states where ERPO data are public, we requested ERPO court records through public records searches or through agencies with access to these data. For California and Washington, ERPO case numbers and non-public identifying information such as respondent name, county, and ERPO date and type were first obtained from the Department of Justice (DOJ) (for California) and the Administrative Office of the Courts (for Washington) through a special request; this information was then used to request the publicly available court records from individual local and county courts throughout the two states.

In Colorado, a local team member contacted each county court to request ERPO records. In Connecticut, the ERPO statute (Connecticut General Assembly 2023 ) specifically requires the court to give notice of the court order to the Department of Mental Health and Addiction Services, and it is through these court notices (that have been maintained since 2013) that the study team accessed the public records. In Florida, we obtained most of the case files through Florida’s secure Comprehensive Case Information System (CCIS), a centralized database of court case information, which streamlined the process of accessing these publicly available records. For a few counties, we obtained the publicly available case files directly from the County Clerks of Court.

In Maryland, at the time of the study, ERPO records were restricted to select entities named in the statute (Brown 2022 ). Working with the Maryland Attorney General’s Office, we requested and obtained ERPO case files from District Courts throughout the State.

It should be noted that ERPO court records are often paper documents and may not be digitally accessible. This is true for California, Maryland, and Washington. Accessing paper copies of ERPO case files in these three states required a significant amount of time and coordination to collect the documents and scan and upload them to secure, password protected file storage systems housed at the collaborating universities in each state. The study teams in Colorado, Connecticut, and Florida gained access to digital copies of case files.

We requested ERPO case files for the time period beginning at ERPO enactment in each state through June 30, 2020 (see Table  1 ) with the exception of Connecticut, where the law took effect in 1999 but full ERPO case reports were only available beginning January 1, 2013. For California, the request process differed slightly. We first obtained identifying information on ERPO respondents through California DOJ and used that information to request the publicly available case files. However, due to California DOJ’s process of overwriting respondents’ older orders with newer orders in the primary file every 3 weeks, it is possible that, in the early days of collecting California’s ERPO case files, we missed cases when an individual was a respondent to more than one ERPO action. Once the California team learned of the California DOJ process in mid-2019, we started requesting ERPO case numbers and respondent identifying information from the California DOJ every 21 days so that we would not miss any order data due to the data overwriting process.

This effectively means we were unable to get case-level data for California prior to mid-2019 and therefore cannot distinguish the number of cases filed. Instead, the California data reflects the number of respondents from ERPO enactment through June 30, 2020, and the number of respondents for whom we coded cases for that timeframe. Additionally, in California, we received few requested case files from the court for cases involving only emergency ERPOs (i.e., those not followed by a temporary or final order) because these orders are granted remotely while the petitioning officer is in the field. As a result, they are typically filed at the local police station or sheriff’s office rather than the courthouse.

We abstracted data from all cases received from each state except Florida. In Florida, the large number of case files received (n = 4695) exceeded our available coding resources; therefore, we abstracted data from a random sample of 50% of cases from all counties with greater than 10 ERPO case files based on the case counts by the Office of the State Court Administrator (OSCA). Fifteen Florida counties had a small number (< 10) of cases based on OSCA counts, and we coded all of those. In total, RAs abstracted data from 6,628 ERPO case files (see Table  1 ) under the 10 domains listed in Table  2 (e.g., criminal legal system; firearm access and possession; and court decisions).

Training and coding procedures

The research team included investigators from nine universities, with members located in each selected state and two additional states. Starting with data collection instruments from two prior ERPO studies (Frattaroli et al. 2020 ; Zeoli et al. 2021 ), we collectively developed the data abstraction instrument for the project by comparing the data elements included on each state’s ERPO petition form and the ERPO eligibility criteria listed in each state’s statute against the existing instruments. This process was lengthy due to the vast differences in ERPO petition forms between, and sometimes within, states. The Principal Investigator (PI) and Co-PI curated a list of common and state-specific candidate abstraction variables and shared it with the state PIs and their teams. After the initial draft of the instrument was created, the PI and Co-PI added, removed, edited, and adjusted the items as necessary given feedback from the research team. Through a series of discussions, the multi-state team refined and finalized the list of data elements that comprised the final data collection instrument.

The goal was to create an instrument that would capture the data needed to understand ERPO use. The final instrument had robust sections related to suicide and interpersonal violence risk, among others (see Appendix A in Supplementary material). For suicide risk, we distinguished among ideation, threats, plans, aborted attempts, and attempts where data were available to disambiguate them. For interpersonal violence risk, we abstracted data on threats and uses of violence, separately, with queries capturing the target of the violence or threat. For both suicide and interpersonal violence risk, we captured whether any of the acts or threats of violence involved a firearm. We also included a variable to specify whether these risk behaviors were part of the event that motivated someone to file an ERPO petition (termed the “precipitating event”). Other sections of the data collection instrument specified the risk context of the situation and captured information about substance use, mental health, criminal history, firearm possession or access, and whether a respondent brandished a firearm. Finally, we included sections about ERPO court processes, whether the ERPO was granted, and whether firearms were removed.

State PIs had the option of adding state-specific variables to the instrument and in Maryland, California, and Connecticut, the PIs did. After agreeing on a good working draft of the instrument, we developed training materials that defined each variable and provided examples of coded excerpts from case files and guidance for abstracting the data that the entire research team reviewed, refined, and approved. We then programmed the data collection instrument in Qualtrics, an online survey software program to which all sites had access. Each state PI was then asked to abstract data from a small number of ERPO petitions from their state to ensure suitability of the instrument (the Maryland team was not able to complete this task due to not yet having access to their state’s ERPO casefiles). Feedback was then incorporated into the instrument.

Each state PI staffed their teams according to their state’s volume of ERPO cases. Due to the differences among state’s ERPO petitions and associated forms within the case files, and the need to include RAs on the Institutional Review Board protocol used by their state PIs, we initially planned for each RA to abstract data only from the state they were hired to staff. In practice, some RAs worked across states to manage the variation in access to case files during the study period. Having RAs who were able to code across states allowed us to keep RAs continuously coding even when files were not available in their home states. Specifically, RAs for Maryland and Florida were combined and coded Florida case files while we waited for access to Maryland case files. When Florida was completed, the RAs moved to code Maryland cases. Importantly, RAs coded only one state at a time to avoid introducing errors associated with switching between state case files and differing forms. The project employed 59 RAs over 17 months to code the 6415 cases.

RAs completed didactic training created by the two project PIs via an online learning management system. The training, a mix of videos, readings, and quizzes, included information about ERPOs, the study aims, the data collection instrument and associated definitions, the process for abstracting data, and information about strategies to reduce the risk and impact of secondary trauma. RAs completed the virtual training modules and passed the quizzes before advancing to practice coding two ERPO case files. After coding two case files, RAs participated in two one-hour synchronous sessions hosted by the project PIs to reinforce the online training, give them an opportunity to ask questions, and to review and discuss the test case coding. Once RAs completed these steps, they were cleared by the PIs to code.

The state PIs then trained RAs cleared for coding in the specifics of each state’s case files and variables. The California team held synchronous training sessions until questions had been resolved and RAs felt comfortable proceeding. For Florida and Maryland, RAs attended two virtual synchronous training sessions, one for each state’s ERPO process. In Colorado, RAs were trained using synchronous training sessions and participated in standing biweekly meetings to discuss abstraction issues and element definitions. For Connecticut, the PI developed a state-specific coding manual instructing RAs where to find data elements in the case files. In Washington, RAs were trained using synchronous training sessions and participated in standing weekly meetings to discuss abstraction discrepancies and definitional disagreements.

When coding began in earnest, the process for reaching reliability differed slightly from state to state, depending on the number of RAs and number of cases to be coded. In Washington, for example, a total of 10% of cases were randomly sampled and coded by all RAs to ensure reliability and consistency. In Colorado, 10% of cases were randomly sampled to be double-coded. In Florida, which had the largest number of cases, coding proceeded one county at a time, and RAs double-coded cases until they graduated to single-coder status. For RAs to graduate, they needed to achieve at least a 0.80 inter-rater reliability score. New RAs and those whose scores were below the target were paired with primary RAs (who had reached the 0.80 threshold) until they, too, reached 0.80.

Data quality and maintaining fidelity to the coding procedures

Because RAs generally coded one state (with the exception of Florida and Maryland RAs), we were unable to quantitatively test reliability of coding between states. Our multiple coding training procedures in which all RAs participated were designed to help ensure consistency. However, due to differences in ERPO documents across states and the lengthy duration of our coding period, it was possible that variations in understanding of variable definitions might have developed among state teams. To combat this possibility, the PI and Co-PI instituted systems to maintain coding pace and consistency among RAs.

Weekly videoconference check-in meetings were implemented, with RAs required to attend at least one meeting each week. Online moderated group chats were used to allow RAs to ask questions as they arose, tagging team members to alert them to the question, enabling them to get answers relatively quickly. The California, Florida, and Maryland teams kept a running document of frequently asked questions that all RAs across states could access during coding. The meetings and group chats served as forums to reinforce training, the coding instrument definitions, troubleshoot coding of complex cases, share consensus with RAs about larger coding questions raised in the online group chats, and develop an inclusive and communicative team dynamic. The check-in meetings and online group chats reduced the number of RA questions needing to be elevated to the PI and Co-PI, maintaining coding pace and consistency.

Prevention and reduction of secondary trauma

Due to the sometimes detailed and graphic descriptions of crises and violence contained in ERPO case files, there was a risk that RAs would experience secondary trauma through reading them. Secondary trauma, also called vicarious trauma, are the effects of indirect exposure to trauma (McCann and Pearlman 1990 ). For example, researchers have reported experiencing physical and emotional symptoms (e.g., sleeplessness, an increased awareness of safety) when conducting research on violence and suicide (Mckenzie et al. 2017 ; Campbell 2002 ). To minimize the risk of secondary trauma, we instituted protocols to limit RA exposure to cases when needed. For example, the protocol dictated that if an RA decided they could not code a specific case, for any reason, that case was reassigned, no questions asked. By guaranteeing we would not ask for an explanation as to why an RA could not code a case, we allowed them to switch out a case without sharing what might be personal information they did not want to disclose to their supervisors. We also encouraged RAs to shift to completing other study tasks when they needed a break from the intensity of coding. In this way, RAs could request time off from coding case files and shift to completing other research-related tasks until they were ready to re-engage with coding. Additionally, at the weekly check-in meetings, space was held to discuss how RAs were handling the emotional and psychological aspects of coding ERPO case files, cultivating an inclusive and communicative environment where RAs would be comfortable sharing with each other. Importantly, PIs and other meeting leads often began the meetings by sharing what they found emotionally difficult in specific cases to set the tone for the meetings and demonstrate that it is normal to be bothered by the case narratives being read.

Furthermore, the online coding training course completed by all RAs included a module on recognizing signs that might indicate secondary trauma and information on what to do when experiencing such symptoms. A licensed clinical social worker on staff with one of the state teams was available to RAs at some RA meetings and on call for individual appointments, should an RA need it. While the social worker did not establish a therapeutic relationship with RAs, they listened, made suggestions and indicated when it might be necessary to seek other resources to help with the psychological load of coding. Additionally, each state team developed a list of available resources (mainly through their universities, for whom the RAs worked) to which RAs could refer. While this research focused on the possibility that RAs might experience vicarious trauma due to their role in reading and abstracting data from the ERPO casefiles, it is important to recognize that even the most seasoned researcher can experience vicarious trauma and benefit from the steps detailed here.

By coordinating data collection on ERPO cases across states, we efficiently achieved greater explanatory power through pooled analyses and direct comparisons than would be possible if we had examined ERPO use in each of these states independently. Analyzing the breadth of violence risks and contexts in which the risks occur in ERPO case files requires attention to detail and standard data collection protocols to be in place and followed. Considering ERPO petitions describe the ways in which the respondent is at risk of harming themselves and/or others, and therefore can contain graphic descriptions of violence and threats (including mass shooting threats, suicide attempts, and domestic violence) conducting research about ERPOs carries risks of secondary trauma. This account of our processes can inform future firearm violence prevention research by providing a reference for how to undertake similar projects in terms of data acquisition, coding, data quality, and strategies to promote health wellness among RAs.

The study used cross-sectional administrative data. Relying on administrative data meant that the processes described are for coding data reported in the case files only. We did not seek out information beyond what was provided (typically solely from the petitioner's perspective) through the ERPO case files. We note that the structure and level of information available in the case files varied across and within states, as well as between petitioner types (law enforcement or civilian). Comparisons of ERPO use across states requires consideration of this variability. In states where law enforcement officers are the only authorized petitioners, information reported about respondents and precipitating events followed a relatively uniform reporting style, although the narrative style of these reports meant that the content was not uniformly consistent in relation to the data points to be abstracted. Where civilians, mainly family members and intimate partners, were authorized to petition, the presentation and type of information included in the petitions varied more significantly.

To our knowledge, this study is the first of its kind to analyze a multi-state sample of ERPOs. The process of standardizing information and abstracting data across states consistently to describe state-level ERPO implementation and assess impacts of the law offers researchers some insight into what such an undertaking involves and provides a foundation on which to interpret findings reported from the six-state study.

Availability of data and materials

A limited dataset generated from ERPO case files will be available at ICPSR upon publication of research from the multi-state study.

Abbreviations

• Extreme risk protection order

Research assistant

Department of Justice

Comprehensive case information system

Principal investigator

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Acknowledgements

We would like to acknowledge and thank our many esteemed research assistants for their dedication and hard work on this project. We could not have done this important work without you!

This project was supported by a grant from the National Collaborative on Gun Violence Research. The funder had no role in the science of the project. The views expressed in this manuscript are the authors’ and do not necessarily reflect the view of the National Collaborative on Gun Violence Research. The views expressed do not represent the Connecticut Department of Mental Health & Addiction Services or Yale University.

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Institute for Firearm Injury Prevention, School of Public Health, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA

April M. Zeoli

The Johns Hopkins Bloomberg School of Public Health, 415 N. Washington, Baltimore, MD, 21205, USA

Amy Molocznik, Elise Omaki & Shannon Frattaroli

New Jersey Gun Violence Research Center, School of Public Health, Rutgers University, 683 Hoes Lane, Piscataway, NJ, 08854, USA

Jennifer Paruk

Department of Emergency Medicine, University of Colorado, 12505 E. 16th Ave, Aurora, CO, 80045, USA

Marian E. Betz

College of Behavioral and Community Sciences, University of South Florida, 4202 E. Fowler Avenue, Tampa, FL, 33612, USA

Annette Christy

Department of Mental Health and Addiction Services, School of Medicine, Yale University, 333 Cedar Street, New Haven, CT, 06510, USA

Reena Kapoor & Michael A. Norko

University of Colorado Anschutz Medical Campus, 13001 East 17th Place, Aurora, CO, 80045, USA

Christopher Knoepke

College of Social Science, Michigan State University, 509 East Circle Drive, East Lansing, MI, 48824, USA

Davis School of Medicine, University of California, 2315 Stockton Blvd., Sacramento, CA, 95817, USA

Veronica A. Pear, Julia P. Schleimer & Garen J. Wintemute

School of Public Health, University of Washington, 3980 15th Avenue NE, Box 351616, Seattle, WA, 98195, USA

Ali Rowhani-Rahbar

Duke University School of Medicine, 2400 Pratt Street, Box 102505, Durham, NC, 27705, USA

Jeffrey W. Swanson

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AMZ performed study conceptualization, investigation, methodology, formal analysis, supervision, data curation, funding acquisition, and was a major contributor in writing and editing the manuscript. AM performed data curation, supervision, project administration, and wrote, reviewed and edited the manuscript. JP performed study investigation, data curation, supervision, project administration, and was a major contributor in writing and editing the manuscript. EO performed data curation, supervision, project administration, and was a major contributor in writing and editing the manuscript. SF performed study conceptualization, investigation, methodology, formal analysis, supervision, data curation, funding acquisition, and was a major contributor in writing and editing the manuscript. MEB performed study investigation, data curation, supervision, project administration, and was a major contributor in writing and editing the manuscript. AC performed study investigation, data curation, supervision, project administration, contributed resources, and was a major contributor in writing and editing the manuscript. RK performed study investigation, data curation, supervision, project administration, and was a major contributor in writing and editing the manuscript. CK performed study investigation, data curation, supervision, and was a major contributor in writing and editing the manuscript. WM performed study investigation, data curation, data analysis, and was a major contributor in writing and editing the manuscript. MN performed study investigation, data curation, supervision, project administration, and was a major contributor in writing and editing the manuscript. VAP performed study investigation, data curation, supervision, project administration, and was a major contributor in writing and editing the manuscript. ARR performed study investigation, data curation, supervision, project administration, and was a major contributor in writing and editing the manuscript. JWS performed study investigation, data curation, and was a major contributor in writing and editing the manuscript. JPS performed study investigation, data curation, and was a major contributor in writing and editing the manuscript. GJW performed study investigation, data curation, and was a major contributor in writing and editing the manuscript. All authors read and approved the final manuscript.

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Correspondence to April M. Zeoli .

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This protocol was approved by the Institutional Review Boards at Michigan State University, Yale University, the Connecticut Department of Mental Health and Addiction Services, the Johns Hopkins Bloomberg School of Public Health, University of Colorado Denver Anschutz Medical Campus, Duke University Health System, University of South Florida, University of California Davis, and University of Washington. The authors certify that the study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

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Zeoli, A.M., Molocznik, A., Paruk, J. et al. A multi-state evaluation of extreme risk protection orders: a research protocol. Inj. Epidemiol. 11 , 49 (2024). https://doi.org/10.1186/s40621-024-00535-z

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Received : 06 June 2024

Accepted : 29 August 2024

Published : 09 September 2024

DOI : https://doi.org/10.1186/s40621-024-00535-z

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The First Successful Whole-Eye Transplant, Over A Year Later

12:05 minutes

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In May of 2023, there was a massive advance in the world of organ transplantation: the first whole human eye and partial face transplant. The man at the center of this procedure is 46-year-old Aaron James, who sustained significant facial injuries from a high-voltage work accident.

At the time, it was unclear just how successful the operation would be. Previous tests in animals had resulted in shrinkage of the transplanted eye, if not outright rejection. But now, more than a year after the transplant, a new paper in the journal JAMA outlines the success of this first-of-its-kind operation. While James cannot see out of his new eye, there is blood flow, normal pressure, and a retinal response to light.

Guest host Kathleen Davis speaks with Dr. Daniel Ceradini, director of research at NYU Langone’s Department of Plastic Surgery and first author of the JAMA study. They discuss the implications this success could have for the future of eye surgery, and the dramatic improvements in James’ quality of life.

Further Reading

• Read about the innovations that made the first whole-eye transplant possible via Nature.

Segment Guests

Dr. Daniel Ceradini is the Director of Research in NYU Langone’s Department of Plastic Surgery in New York, New York.

Segment Transcript

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Meet the Producer

About kathleen davis.

Kathleen Davis is a producer at Science Friday, which means she spends the week brainstorming, researching, and writing, typically in that order. She’s a big fan of stories related to strange animal facts and dystopian technology.

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