Gender distribution in psychiatry journals' editorial boards worldwide
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it