Does gender influence leadership roles in academic surgery in the United States of America? A cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Gender disparity remains prevalent in the field of academic surgery with an under-representation of women at senior leadership ranks. A wide variety of causes are reported to contribute to this gender-based discrimination but a current quantitative analysis in the US has significant importance. This cross-sectional study aims to document gender disparity in academic and leadership positions in surgery as well as its relationship with scholarly productivity. MATERIAL AND METHODS: The American Medical Association's Fellowship and Residency Electronic Interactive Database (FREIDA), was used to identify General Surgery programs. Each institution's website was used to identify its faculty's primary profiles for data collection. Individuals with an MD or DO, and an academic ranking of Professor, Associate Professor or Assistant Professor were included. Academic productivity was quantified by recording H-index, number of publications, number of citations, and years of active research of a physician. All statistical analysis was performed on SPSS Statistics version 20.0. RESULTS: A total of 144 academic programs were including in our analysis constituting 4085 surgeons, only one-fifth (n = 873, 21%) of which were women. Furthermore, only 19% of all leadership positions were assumed by female surgeons. Leadership positions and academic rank correlated significantly with increasing research productivity. The difference in H-index between genders was statistically significant (P < 0.05) with men possessing a higher median for H-index [13] than women [9]. Transplantation Surgery [17] had the highest median H-indices for female surgeons. Male surgeons (n = 18) were twice as likely to be Departmental Chairs as their female counterparts (n = 9). However, female surgical oncologists held the highest proportion of leadership positions (31%). CONCLUSION: A significant gender-based disparity was found in leadership positions and academic ranks. Research productivity appeared to be integral for academic and leadership appointments. Institution-level measures that enhance support, mentorship, and sponsorship for women are imperative to achieve overall parity in general surgery.
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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 | 0.000 |
| 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) | 0.002 | 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".