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Record W7115724488 · doi:10.48448/3v4a-5559

Author Responses to Editorial Guidance on Reporting of Sex, Gender, Race and Ethnicity Data

2025· other· W7115724488 on OpenAlexaboutno aff

Bibliographic record

VenueUnderline Science Inc. · 2025
Typeother
Language
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsRace (biology)Ethnic groupGuidelineChinaEquity (law)Gender equity

Abstract

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Mabel Chew,<sup>1</sup> Taissa Vila,<sup>2</sup> Jashelle Caga-Meller,<sup>3</sup> Zoë Mullan,<sup>4</sup> Diana Samuel<sup>5</sup> <h4>Objective</h4> As part of The Lancet<i> </i>Group’s commitment to advancing equity, diversity, and inclusion, we implemented the Sex and Gender Equity in Research (SAGER) guidelines<sup>1</sup> in 2023, and <i>The Lancet</i> guidance on reporting race and ethnicity<sup>2</sup> in June 2024 across 24 Lancet Group journals. Although data exist on the reporting of race and ethnicity in published articles after similar guidelines were launched,<sup>3</sup> less is known about how authors view and engage with guidelines, which is crucial to implementing and refining editorial policy. Thus, we aimed to examine authors’ awareness of and responses to these guidelines and identify challenges to guideline implementation. <h4>Design</h4> Corresponding authors of research or review articles submitted to a <i>Lancet</i> journal, who had received a request for revision between July and December 2024, were invited to participate in an online survey in January 2025. The survey asked authors about their awareness of the guidelines, changes they made to their manuscripts in response to editorial advice, the ease with which they made those changes, any challenges encountered, and how these guidelines might influence their next project. <h4>Results</h4> <i> </i>The survey response rate was 22% (484 of 2185 invitations). The largest proportion of respondents was from the US (68 [14%]), followed by China (65 [13%]), the UK (59 [12%]), Australia (30 [6%]), the Netherlands (27 [6%]), Germany (21 [4%]), Canada (19 [4%]), and Sweden (17 [4%]). Fifty-four percent identified as a man (n=262), 41% as a woman (n=197), 1% as non-binary or gender diverse (n=5), and 4% preferred not to say (n=20); (64%) were involved in research and/or development, and 191 (40%) were senior researchers or in middle management. Of the 484 respondents, 193 (40%) and 220 (46%) were aware of the sex and gender and race and ethnicity reporting guidelines before submitting, respectively. A total of 246 respondents (51%) and 266 (55%), respectively, were not required to collect these data, and 9 (2%) and 33 (7%) were not permitted to collect these data. Among 153 respondents (32%) who amended their manuscript as a result of the sex and gender guidance, 104 found this easy or very easy to do. Fewer respondents (90 [19%]) made changes in response to the race and ethnicity guidance, with 55 finding this easy or very easy. <b>Box 25-1122</b> includes open-text responses on challenges in addressing these guidelines. Approximately one-half of respondents said they were likely or very likely to do things differently in their next project as a result of these guidelines (255 [53%] and 221 [46%] for each guidance, respectively). <h4>Conclusions</h4> Less than one-half of the authors surveyed reported awareness of sex and gender or race and ethnicity reporting guidelines before submission, suggesting that increasing awareness could enhance engagement. Most authors who amended their manuscript in response to these guidelines found this to be easy. The challenges identified provide opportunities for journals to refine editorial processes. https://assets.underline.io/markdown_image/1/image/f556edaec2af5850b8b81ae3c23ff545.png <h4>References</h4> 1. Heidari S, Babor TF, De Castro P, Tort S, Curno M. Sex and gender equity in research: rationale for the SAGER guidelines and recommended use. <i>Res Integr Peer Rev</i>. 2016;1:2. doi:10.1186/s41073-016-0007-6 2. Chew M, Samuel D, Mullan Z, Kleinert S; Lancet Group for Racial Equity (GRACE). The Lancet Group’s new guidance to authors on reporting race and ethnicity. <i>Lancet</i>. 2024;403(10442):2360-2361. doi:10.1016/S0140-6736(24)01081-X 3. Flanagin A, Cintron MY, Christiansen SL, et al. Comparison of reporting race and ethnicity in medical journals before and after implementation of reporting guidance, 2019-2022. <i>JAMA Netw Open</i>. 2023;6(3):e231706. doi:10.1001/jamanetworkopen.2023.1706 <sup>1</sup><i>The Lancet, </i>Elsevier Australia, Chatswood, Sydney, Australia, mabel.chew@lancet.com; <sup>2</sup><i>The Lancet Regional Health—Americas, </i>Rio de Janeiro, Brazil; <sup>3</sup><i>The Lancet Regional Health—Western Pacific</i>, Sydney, Australia; <sup>4</sup><i>The Lancet Global Health</i> London, UK; <sup>5</sup><i>The Lancet Digital Health</i>, London, UK. <h4>Conflict of Interest Disclosures</h4> Mabel Chew is a member and former co-chair of The Lancet Group for Racial Equity and has received funding for conference travel expenses from the World Conference on Research Integrity, Committee on Publication Ethics, and Nuffield Department of Primary Care Health Sciences. Taissa Vila is co-chair of and a member of The Lancet Group for Racial Equity. Jashelle Caga-Meller is honorary clinical senior lecturer at the Faculty of Medicine and Health, University of Sydney, Australia, and a member of The Lancet Group for Racial Equity. Diana Samuel is a member and former co-chair of The Lancet Group for Racial Equity, and a member and former Chair of the European Association of Science Editors’ (EASE) EDI Committee. <h4>Acknowledgments </h4> We thank all authors who responded to the survey; Marco Conforti for database support; Louise Hall and Adrian Mulligan for their help in conducting the survey and in preliminary reporting and analysis; Pooja Jha, Rupa Sarkar, Lan-Lan Smith, Richard Horton, and members of the <i>Lancet’s</i> Group for Racial Equity (GRacE) for advice and support.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.051
metaresearch head score (Gemma)0.161
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Open science
Consensus categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Open science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.433
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0510.161
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0040.007
Science and technology studies0.0010.006
Scholarly communication0.0010.001
Open science0.0070.009
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.140
GPT teacher head0.425
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
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