Gender distribution in psychiatry journals' editorial boards worldwide
Bibliographic record
Abstract
Gender disparity has been documented in advanced doctoral degrees, research, and academic positions, and therefore, it can logically be deduced that the gender disparity would be found in journals' editorial boards. In this study, we sought to determine the gender distribution in editorial boards of psychiatry journals worldwide. We also studied the academic achievements of editorial board members by comparing professional background, education level, and research productivity indices. We analyzed the gender of editorial members of 119 psychiatry journals from Clarivate Analytics' Journal Citation Reports. Our data included 8423 editorial board members from which we randomly selected 10% editorial board members to represent the full sample for further analyses. Overall, women represented 30.4% of editorial board and approximately 30% in each category: (1) Editor-in-chief/deputies, (2) Associate/section editors, (3) Editorial board*, and (4) Advisory board. The majority (65%) of men were M.D. psychiatrists, and women (58%) were Ph.D. psychologists. Women in editorial leadership positions (Category 1 & 2) were correlated with fewer women in editorial or advisory boards. Women had half the mean number of publications than men while serving journals with approximately the same mean impact factor. Our study results show that, besides gender disparity, gender bias does not exist in the psychiatry journal editorial boards. Given the implication of the editorial board position on science, academic advancement, and networking, this disparity remains detrimental to achieving equity, diversity, and inclusion in academic psychiatry.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".