Gender Disparity in Academic Trauma Surgery: The Current State of Affairs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it