Diversity in US medical school department chairs: 45-year retrospective analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it