Gender Disparity in Academic Trauma Surgery: The Current State of Affairs
Bibliographic record
Abstract
Introduction Despite the increasing number of female surgeons in general surgery programs, women are still inadequately represented in leadership positions. This study aims to investigate the magnitude of gender bias in university-based trauma surgery fellowship programs and leadership positions in the United States of America. Material and Methods FRIEDA was used to identify trauma surgery programs. A thorough website review of each program obtained further information on faculty members, including their name, age, gender, and faculty rank. Trauma surgeons with an MD or DO qualification and a faculty rank of Professor, Associate Professor, or Assistant Professor were selected for inclusion in this study. SCOPUS was used to assess the H-index and the number of publications and citations of surgeons. Results The total number of programs included was 136, consisting of 715 faculty members. Less than a quarter (n = 166; 23.2%) comprised females and less than one-fifth (n = 30; 19%) of female surgeons were Professors. The difference in the research productivity of male and female trauma surgeons was statistically significant ( P < .05), with the average H-index being 10 vs 7.5, respectively, amongst the top 50 surgeons of both genders. Based on a multiple regression analysis, academic rank was significantly associated ( P < .05), and gender was not significantly associated ( P > .05) with H-index. Conclusion Gender disparity exists in the field of trauma surgery, as noted in senior faculty ranks and leadership positions. Female-inclusive state policies, appropriate mentorship, and supportive institutions can help to bridge this gap.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".