Sex, Race, and Ethnicity in Academic Dermatology in the United States: A Longitudinal Analysis
Bibliographic record
Abstract
INTRODUCTION: Sex and racial/ethnic disparities in academic medicine are an intractable and inordinate reality. The purpose of our study was to analyze the 56-year trends in sex and racial/ethnic demographics in academic dermatology in the United States. METHODS: Data from the Association of American Medical Colleges Faculty Roster between 1966 and 2021 were analyzed to trend the representation of sex and race/ethnicity by academic ranks, leadership positions, and tenure statuses using linear regression. In addition, differences in representation using 56-year means were assessed using the Mann-Whitney test for sex and Kruskal-Wallis test followed by the Dunn multiple comparisons test for race/ethnicity. RESULTS: In 2021, academic dermatologists were 66.71% (1072/1607) White, 20.97% (337/1607) Asian, 2.86% (46/1607) Black, and 2.49% (40/1607) Hispanic. From 1966 to 2021, the total number of academic dermatologists increased from 107 to 1607 [mean +26.92 (95% CI 25.15-28.70) per year, P < .001]. Specifically, annual proportional increases for groups were women +0.93 (0.90-0.96)% ( P < .001); Asians +0.38 (0.34-0.42)% ( P < .001); Blacks +0.01 (0.01-0.02)% ( P < .001); and Hispanics +0.01 (0-0.02)% ( P = .001). DISCUSSION: Despite these increasing trends, in 2021, the most recent year for which data were provided, there remained a far greater proportion of male and/or White physicians in senior academic ranks (65.35% male and 75.85% White professors), leadership positions (79.52% male and 66.27% White chairpersons), and tenure (67.17% male and 77.78% White). Although there have been efforts to improve diversity and inclusion in academic dermatology over the 56-year study period, sex and racial/ethnic disparities persist.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".