Faculty Diversity Trends in Academic Anesthesiology by Demographics in the United States, 1977–2021
Bibliographic record
Abstract
BACKGROUND: This surveillance study sheds light on the demographic trends in academic anesthesiology and highlights the shifts that have taken place over 4 consecutive decades. METHODS: The data for academic anesthesiology faculty were self-reported and obtained from the annual Faculty Roster report of the Association of American Medical Colleges (AAMC) from 1977 to 2021. After determining the distribution of academic degrees, academic rank, chair position, and tenure status over time, the percentage composition for each category was calculated for 44 years. The temporal trends were depicted by plotting the counts and proportion changes. At the same time, the progress in terms of racial representation was illustrated by graphing the absolute changes in the percentage composition. RESULTS: Despite an overall increase in absolute composition and percentage of women in academic anesthesiology from 20.8% to 35.7%, women remained underrepresented in academic degree attainment, senior academic ranks, and leadership positions. Faculty identifying as Black or African American increased from 1.3% to 4.3%, while Hispanic, Latino, or Spanish-origin faculty grew from 1.2% to 5.2%, representing modest growth in these underrepresented groups over the span of 4 decades. CONCLUSIONS: Despite an increase in the count of women and underrepresented minority faculty within academic anesthesiology since the 1970s, the persistence of imbalances related to gender, ethnicity, and race was observed, in senior academic ranks and leadership roles.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".