Gender Disparity in Leadership Positions of General Surgical Societies in North America, Europe, and Oceania
Bibliographic record
Abstract
Background Despite the number of female medical-school applicants reaching an all-time high and the increasing number of females in surgical training, males retain an overwhelming majority in senior surgical academic positions and formal leadership positions. This study aims to better understand the extent of and influences for gender disparity in general surgical societies throughout North America, Europe, and Oceania. Methods Data collection for this retrospective cross-sectional study took place between June and December 2017. Committee and subcommittee members from the eight selected general surgical societies that met the inclusion criteria (n = 311) were compiled into an Excel spreadsheet in which the data was recorded. Analyzed metrics included university academic ranking, surgical society leadership position, h-index, number of citations, and total publications. SCOPUS database (Elsevier, Amsterdam, Netherlands) was used to generate author metrics, and STATA version 14.0 (StataCorp, College Station, TX) was used for statistical analysis. Results Overall, 83.28% of members of the entities we studied were male and 16.72% were females. Males had significantly higher representation than females in all societies (Pearson chi2 = 29.081; p-value = 0.010). Females were underrepresented in all society leadership positions and university academic rankings. Male members had a higher median h-index, more number of citations, and more total publications. Conclusions The composition of the general surgical societies included in this study demonstrated significant gender disparity. Female inclusivity initiatives and policies must be initiated to promote greater research productivity and early career opportunities for female surgeons in the specialty of 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".