Gender Disparity in Leadership Positions of General Surgical Societies in North America, Europe, and Oceania
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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