Sex Differences in Faculty Rank and Leadership Positions Amongst Hematologists and Oncologists in United States: A Cross-Sectional Study
Bibliographic record
Abstract
Introduction: We have previously reported underrepresentation of female faculty at senior academic ranks in hematology/oncology (H/O). In this analysis we aimed to investigate the influence of sex in attaining leadership positions amongst academic hematologists/oncologists in United States. Methods: Faculty members were identified at 146 H/O fellowship programs listed on fellowship and residency electronic interactive database (FREIDA.) Data was collected on demographics, academic rank and research output using Doximity and Scopus databases. We compared the unadjusted characteristics of men and women by using two-sided t-tests and χ2 tests where appropriate. In primary analysis, logistic regression models were used to evaluate sex differences on probability of having full professorship (versus assistant and associate professorship) and of achieving leadership positions including division chief, Program Director (PD) and Associate Program Director (APD). Adjusted models included the following variables: clinical experience in years, number of publications, h-index, appointment at top 20 hospital, clinical trial investigator status and National Institutes of Health funding. Stratified analysis was performed adjusting for duration of clinical experience (≤15 vs >15 years) Results: Fewer women were full Professors (21.9% vs 78.1%), division chiefs (16.7% vs 83.3%), and PDs (30.5% vs 69.5) but the number was similar for Associate Program Directors (47.1% vs 52.9%). In a univariate unadjusted model, women were less likely to be full professors compared to men (OR 0.39; 95% confidence interval [CI], 0.31-0.48; P<.001). However, in the multivariable adjusted model no statistically significant sex difference in full professorship was found (OR 1.05; 95% CI 0.71, 1.57; P=.85; Table). The likelihood of full professorship was positively associated with clinical experience in years, number of first/last author publications, h-index, and being a primary investigator on at least one clinical trial.In a univariate unadjusted model, women were less likely to be division chiefs as compared to males (OR 0.35; 95% CI, 0.16, 0.80; P=.01). However, in the multivariable adjusted model, there was no statistically significant sex difference in achieving the position of division chief (OR 0.57; 95% CI 0.20, 1.58; P=.28; Table). No significant difference was found between females and males for being program directors or associate program directors in both univariate and multivariate analysis. Similarly, a stratified analysis adjusting for duration of clinical experience (≤15 vs >15 years) found no significant sex differences in attaining leadership position (Table) Conclusion: We found that women are underrepresented at higher academic ranks and in leadership positions in hematology/oncology, but that sex is not a significant negative predictor to women obtaining leadership positions after correcting for traditional predictors of academic success. However, "non-traditional" and therefore less measurable and analyzable factors such as networking, mentorship, sponsorship, gender bias, balancing work and home responsibilities and many others may contribute and should be further investigated. Disclosures No relevant conflicts of interest to declare.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".