Women in Academic Medicine Leadership: Has Anything Changed in 25 Years?
Bibliographic record
Abstract
Over the past 25 years, the number of women graduating from medical schools in the United States and Canada has increased dramatically to the point where roughly equal numbers of men and women are graduating each year. Despite this growth, women continue to face challenges in moving into academic leadership positions. In this Commentary, the authors share lessons learned from their own careers relevant to women's careers in academic medicine, including aspects of leadership, recruitment, editorship, promotion, and work-life balance. They provide brief synopses of current literature on the personal and social forces that affect women's participation in academic leadership roles. They are persuaded that a deeper understanding of these realities can help create an environment in academic medicine that is generally more supportive of women's participation, and that specifically encourages women in medicine to take on academic leadership positions.
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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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 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".