Leadership Amongst Regional and National Surgical Organizations: The Tides Are Changing
Bibliographic record
Abstract
INTRODUCTION: Leadership amongst professional organizations is a key opportunity for scholarly activity which is essential for academic advancement. Our objective was to examine the differences between men and women in leadership within surgical organizations. METHODS: Credentials were obtained through an internet search. Variables included organization type, leadership role, gender, advanced degree, medical school graduation year, and publications. A bivariate analysis was performed between genders. A p-value <0.05 was considered statistically significant. RESULTS: Five hundred forty-three leaders were identified in 43 surgical organizations. There was a significant difference in the number of male and female leaders (72.7% vs 27.3%, p=0.016). Women were most likely to hold the role of "Other", which consisted of lower-level leadership roles including committee chair positions and resident and medical student delegates (35.5%). Fewer women had publications (85.8% vs 92.9%, p=0.01), more women had advanced degrees (24.5% vs 17.0%, p=0.049), and women were involved earlier in their careers (5.9 years, 95% CI 4.1-7.7 years, p<0.001) than their male colleagues. CONCLUSION: Gender disparity in leadership of surgical organizations exists. Women are involved earlier in their careers and hold lower-level leadership positions reflecting potential for increased involvement in high-level leadership roles in the future. Data need to be trended to discern if women in surgical organizations rise within leadership roles as more women continue to enter surgical subspecialties.
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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 | 0.008 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".