Diversity in US medical school department chairs: 45-year retrospective analysis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the trends in sex and race/ethnicity demographics of department chairs at US medical schools over 45 years. DESIGN: This was a longitudinal retrospective analysis of the Association of American Medical Colleges database. SETTING: The study analysed the sex and race/ethnicity of department chairs in US medical schools. PARTICIPANTS: Department chairs were classified by sex and self-reported race/ethnicity. Data from 1977 to 2022 were used to evaluate changes in the demographic composition of leadership roles over time. EXPOSURE: Identifying as female and/or as part of an under-represented in medicine group. MAIN OUTCOMES AND MEASURES: The outcome measures were demographic (ie, sex, race and ethnicity) trends among department chairs. RESULTS: The analysis depicted under-representation of women and racial minorities in department chairs. A notable increase was noted in the number of Asian, Black or African American, and Hispanic or Latino department chairs. However, this was outnumbered by the number of white individuals in leadership positions. CONCLUSION AND RELEVANCE: The end of affirmative action is expected to jeopardise the progress made and has the potential to perpetuate the lack of diversity in the department chair 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".