Faculty diversity trends in physical medicine and rehabilitation by gender, race, and ethnicity in the United States, 1977–2021
Bibliographic record
Abstract
BACKGROUND: This study describes the gender and racial/ethnic trends in academic physical medicine and rehabilitation (PM&R) and the shifts that have taken place in more than 4 decades. OBJECTIVE: To gauge the diversity in gender and race/ethnicity across academic degrees, academic ranks, chair positions, and tenure status in the academic workforce of PM&R. DESIGN: Surveillance study. SETTING AND METHODS: The data for academic PM&R faculty were self-reported and obtained from the annual Faculty Roster report of the Association of American Medical Colleges from 1977 to 2021. MAIN OUTCOME MEASURES: To compare the distribution of academic degree, rank, chair position, and tenure status over time, the percentage composition for each category was calculated for a period of 45 years. The temporal trends were depicted by plotting the counts and proportion changes, and the progress in terms of racial representation was illustrated by graphing the absolute changes in the percentage composition. RESULTS: Despite an overall increase in the representation of women, women remained underrepresented in the full professor rank in 2021, at only 32.1% of full professors. The instructor category was the only category in which the proportion of women faculty was higher in 2021 (62.8%) than in 1977 (58.5%). Asian faculty had the greatest increase in representation at all ranks, with the proportion of Asian full professors increasing from 1.8% to 11.4%, associate professors increasing from 7.4% to 14.4%, and assistant professors increasing from 11.2% to 20.2%. Women's representation as department chairs increased from 12.5% to 23.7% and Asians from 2.5% to 15.3%. CONCLUSION: Overall, although there was an increase in the number of women and underrepresented minority faculty in academic PM&R over the study period, disparities based on gender and ethnicity/race persisted, particularly in higher academic ranks and leadership 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".