Prevalence of Gender Disparity in Professional Societies of Family Medicine: A Global Perspective
Bibliographic record
Abstract
Introduction Gender disparity in academic and organizational settings has been the topic of numerous studies, which have attributed under representation of females within medicine to both individual and institutional reasons. The main objective of our study was to assess gender disparity in leadership positions in committees of professional societies of family medicine (FM). Methods In this cross-sectional observational study, we collected publicly available information from 3 major FM societies (College of Family Physicians of Canada, the Royal Australian College of General Practitioners, and the World Organization of Family Doctors) and also collected the academic/leadership information for each committee member, including bibliometric parameters of their research productivity. Results In total, our sample size was 960 and composed of 58% men (556) and 42% women (404). There was a significant difference found in all the research productivity variables. Men had a greater number of publications, number of citations, years since first publication, years of active research, and had a larger h-index. Conclusion In conclusion, gender disparity within FM societies is less significant compared to other professional medical societies and creating an environment that supports women and supports research can aid in achieving gender parity.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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".