Organizational Leadership Gender Differences in Medical Schools and Affiliated Universities
Bibliographic record
Abstract
Objective: To compare gender compositions in the leadership of the top 25 medical schools in North America with the leadership of their affiliated university senior leadership and other faculties. Materials and Methods: This retrospective cross-sectional observational study used publicly available gender data from 2018 to 2019 of universities drawn from the U.S. News Best Global Universities for Clinical Medicine Ranking report. Gender compositions in eight leadership tiers from senior leadership to medical school department directors were analyzed. Data analysis included gender compositions by leadership tier and faculty. Results: Male representation is greater at higher leadership tiers, with the largest imbalance being at the level of medical school department heads. The faculty of medicine has more men in leadership positions than the average of the other faculties ( p = 0.02), though similar to schools of engineering, business, dentistry, and pharmacy. Across the eight leadership tiers, a significant trend exists between tier and proportions, indicating that male representation was greater at higher tiers ( p < 0.001). No correlation was found between a university's leadership gender composition and its ranking. Conclusion: The under-representation of women is greater in medical school leadership than the leadership of their affiliated universities. The faculty of medicine has greater male over-representation than the average of the other 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".