Sticky Floor and Glass Ceilings in Academic Medicine: Analysis of Race and Gender
Bibliographic record
Abstract
Purpose This paper examines the changes in the representation of women and racial minorities in academic medicine, compares the proportion of minorities in medicine and the general United States (US) population, and discusses potential explanations for observed trends. Methods A retrospective cross-sectional analysis of the Association of American Medical Colleges (AAMC) database was done and used to collect data on the gender and race of physicians in academic medicine. Data was collected for instructors, assistant professors, associate professors, full professors, and chairpersons from 2007 to 2018, and trends were presented. Results White physicians represented most academic physicians at every academic level, peaking in proportion at 82.74% of chairpersons and were lowest at the level of instructor at 59.30%. A similar distribution existed when gender was compared, with men comprising 84.67% of chairpersons and forming the majority at levels of full, associate, and assistant professors. However, most physicians at the level of instructors are women at 55.44%. Conclusions Though women and racial minorities have gained greater representation in academic medicine over the past decade, high-level academic positions are not as accessible to them. Existing efforts of advocacy for women and minority races have proven fruitful over the past decade, but much more work needs to be done.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".