Extensive gender disparity in top medical schools and their affiliated dermatology departments: a cross-sectional study
Bibliographic record
Abstract
Background Previous studies demonstrate female under-representation in top medical school leadership and dermatology departments, although separately. Here, we investigate the extent and interplay of gender disparity between these two bodies. Objective To compare the extent of gender disparity among top 15 US medical schools with affiliated dermatology programmes. Methods Cross-sectional study conducted in 2022. Faculty gender, academic rank, leadership position and membership of medical school leadership or affiliated dermatology department were extracted from public institutional sources. Research metrics (h-index, citations, publication span and publication counts) were collated using Elsevier’s SCOPUS tool. Results From 1243 individuals (31.7% women), 840 held medical school leadership positions and 403 were affiliated dermatology faculty. Rank biserial correlation indicated a significant relationship of male gender with higher academic rank (r=−0.305, p<0.001), leadership position (r=0.095, p=0.004) and scholarly metrics. More medical leadership individuals had higher academic rank than dermatology faculty; we, therefore, hypothesise a pipelining of rising departmental faculty into leadership positions. Limitations Public faculty listings seldomly reported leadership appointment age and length, career duration and mid-career breaks. Conclusion Continued diversity efforts are recommended to improve female under-representation in medical school leadership and affiliated dermatology faculties.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".