Influences for Gender Disparity in Academic Family Medicine in North American Medical Schools
Bibliographic record
Abstract
Background Women physicians continue to comprise the minority of leadership roles in Academic Family Medicine (AFM) faculty across North American medical schools. Our study quantified the current state of gender disparity by analyzing academic position, leadership ranking, and research productivity. Methods We generated a database for 6,746 AFM faculty members. Gender and academic profiles were obtained for 2,892 academic ranks and 1,706 leadership roles by searching faculty listings enlisted in Fellowship and Residency Electronic Interactive Database (FREIDA) and Canadian Resident Matching Service (CaRMS). To measure research productivity, we obtained bibliometric data: h-index, citations, and tenure from 2,383 faculty members using Elsevier's SCOPUS archives. Data analysis and h-index were formulated using Stata version 14.2 (StataCorp LP, College Station, TX). Results Our results indicated that women hold 46.11% (3,110/6,746) of faculty positions. The proportional composition decreased with increasing academic ranking (49.84% assistant, 46.78% associate, and 41.5% full professor). The same decreasing trend was demonstrated with leadership rank (57.14% minor leadership, 47.65% second-in-command, and 36.61 first-in-command). Compared to their gender counterparts, women in AFM demonstrated lower publication productivity as measured by citation number (p=0.04) and years of study (p=0.008). The final prediction equation model after multivariable analyses included gender, publications, citations, country of graduation, and years of active research (p<0.05). Conclusions The composition of academic family medicine faculty members included in this study demonstrated gender disparity. Inclusivity initiatives and policies to tackle the issue of female retention, promotion, and recruitment need to be further explored.
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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 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".