Trends in the proportion of women as reviewers, editors, and editorial board members of 15 North American and British medical journals from 2014 to 2019: A retrospective study
Bibliographic record
Abstract
Background and objective: There is persistent men-dominated gender disparity in medical academia. Predominance of men in the editorial makeup of medical journals might contribute to this inequity. This retrospective study (2014–2019) sought to evaluate gender representation in reviewers, editors, and members of the editorial boards in 15 leading medical journals from the United States, Canada, and the United Kingdom. Methods: We surveyed lists of reviewers, editors, and editorial board members from seven journals of internal medicine, a specialty dominated by men; three journals of obstetrics and gynaecology and two of paediatrics, specialties dominated by women; and three journals of psychiatry, a gender-balanced specialty. Information from publicly available resources was used to infer gender, and the percentages of women were calculated. Trends over time were characterized by changes in these percentages from year to year through the linear regression line fitted to the data for each journal. Results: Journals of women-dominated specialties had significantly higher proportions of women reviewers than those of men-dominated or gender-balanced specialties, with mean percentages (95% confidence interval) of 45.8% (40.5%–51.1%), 28.0% (22.3%–33.7%), and 33.8% (27.6%–40.1%), respectively (p <0.001). The proportion of women editors and editorial board members showed no statistically significant differences across the three specialties, and the percentage of women reviewers, editors, and editorial board members increased only slightly over time. Conclusion: These results suggest that the fifteen journals are yet to achieve gender parity in their reviewers, editors, and editorial board members, and continued efforts are needed to achieve gender balance in those three groups of medical academia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".