Gender Differences in Faculty Rank and Leadership Positions Among Hematologists and Oncologists in the United States
Bibliographic record
Abstract
PURPOSE: Gender disparity persists in academic medicine. Female faculty are underrepresented in leadership positions and have lower research output. We studied gender differences in faculty rank and departmental leadership and contributing factors among academic hematologists and oncologists in the United States. METHODS: For clinical faculty at 146 hematology or oncology fellowship programs listed in the Fellowship and Residency Electronic Interactive Database, we collected data on demographics, academic rank, and research output using the Doximity and Scopus databases. We compared unadjusted characteristics of men and women by using 2-sided t tests and χ2 tests where appropriate. To predict probability of full professorship or leadership position among men versus women, we performed multivariable logistic regression analysis adjusted for clinical experience in years, number of publications, h-index, clinical trial investigator status, National Institutes of Health funding, and workplace ranking (top 20 v not). RESULTS: Two thousand one hundred sixty academic hematologists and oncologists were included. Women composed 21.9% (n = 142) of full professors, 35.7% (n = 169) of associate professors, and 45.4% (n = 415) of assistant professors. Thirty percent (n = 70) of departmental leaders were women. Female faculty, compared with male faculty, had a lower mean h-index (12.1 v 20.9, respectively; P < .001) and fewer years of professional experience since fellowship (10 v 16 years, respectively; P < .001). After adjusting for duration of clinical experience, academic productivity, and workplace ranking, the odds of obtaining professorship (odds ratio [OR], 1.05; 95% CI, 0.71 to 1.57; P = .85) or divisional leadership (OR, 0.57; 95% CI, 0.20 to 1.58; P = .28) for female physicians were not different compared with male physicians. CONCLUSION: Gender disparity exists in senior ranks of academic hematology and oncology; however, gender is not a significant predictor in achieving professorship or department leadership position.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".