Physical Medicine and Rehabilitation Faculty Diversity Trends by Sex, Race, and Ethnicity, 2007 to 2018 in the United States
Bibliographic record
Abstract
INTRODUCTION: Sex and race/ethnicity disparities persist in academic Physical Medicine and Rehabilitation (PM&R). This study contributes to the current body of knowledge by demonstrating changes in academic PM&R by sex and race/ethnicity in multiple categories over a 12-year period. OBJECTIVE: To evaluate workforce disparities in academic PM&R by measuring sex and race/ethnicity diversity in academic degree, rank, and tenure status. DESIGN: Surveillance study. SETTING AND METHODS: Self-reported data for PM&R from the Association of American Medical Colleges (AAMC) annual Faculty Roster report from 2007 to 2018. MAIN OUTCOME MEASURES: The 12-year average percentage composition in academic degree, rank, and tenure status was calculated to compare the overall distribution. Counts and proportion changes were plotted to depict the temporal trends. Absolute changes in racial percentage composition were graphed to highlight the progress. RESULTS: From 2007 to 2018, the increase by sex was roughly equal (male = 216; female = 236), whereas most of the increase was in White faculty (207). The representation of female and Underrepresented in Medicine (URiM) faculty decreased as academic level advanced. Instructors is the only category with a higher proportion of female faculty, from 2007 (53%) to 2018 (59.3%), whereas male faculty occupied over 75% of the full professor positions at any time. Among the non-White faculty, Asian faculty had the greatest increase in proportion of full professors (3.7% to 10%) and Hispanic/Latino faculty in associate professors (2% to 7.1%), whereas full professors who were Black/African American decreased from 4 persons (2.5%) to 2 persons (0.8%). CONCLUSION: An increase in total number of female and URiM faculty was observed in academic PM&R over 2007 to 2018, but sex and ethnicity/race disparities persisted, especially in higher ranks and leadership positions. For non-White faculty, greater disparities existed, pointing toward the need to target challenges faced by URiM race/ethnicity status.
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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 | 0.003 |
| 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) | 0.003 | 0.001 |
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".