Flattening the hierarchies in academic medicine: the importance of diversity in leadership, contribution, and thought
Bibliographic record
Abstract
The redistribution of several oil portraits of physician leaders from an auditorium at Brigham and Women’s Hospital to other locations has been the source of much debate on social media. Such portraits—ubiquitous in academic medical institutions—speak of pride, achievement, and institutional history, but to some, represent a lack of gender and racial diversity in academic leadership. To others, the portraiture represents the hierarchical structures in academic medicine that disproportionately reward physician leaders and leave the diverse contributions of the healthcare workforce under-recognized. The gap between the face of leadership and the healthcare workforce that educates, researches, and delivers care has been increasingly obvious in recent years. Women and minorities have made major contributions in medicine, but have typically been under-recognized, under-promoted, and denied access to positions of power. According to the 2014 Association of American Medical Colleges report, although approximately half of medical school graduates in the USA were women, only 16% of medical school deans were women.1 The statistics in Canada are even more concerning; data from the 2015 Canadian Medical Education Statistics revealed that 55% of Canadian medical school graduates were women, yet only one dean and two chairs of medicine across the country were women.2
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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 | 0.039 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.021 | 0.047 |
| Scholarly communication | 0.022 | 0.013 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".