Gender Distribution of Faculty Is Strongly Correlated With Resident Gender at Canadian Radiology Residency Programs
Bibliographic record
Abstract
Objective: Women are underrepresented in radiology overall, in radiology subspecialties, and in radiology leadership and academic positions. It is unclear why this disparity persists despite greater gender diversification in medicine. We sought to determine if a correlation exists between the proportion of female faculty at an institution, and the proportion of female residents in the associated residency program across Canada. Methods: Faculty gender for each Canadian Diagnostic Imaging Residency Program was obtained through publicly available sources (departmental websites and provincial physician registries) in the fall of 2020. Resident gender data was obtained through a survey emailed to programs following the April 2021 CaRMS match. Data was analyzed using Pearson’s correlation coefficient. Research ethics approval was obtained. Results: Faculty information was available for 15 of the 16 Canadian radiology residency programs (94%) and resident information was obtained for 16 programs (100% response rate). Overall, women accounted for 31.4% of radiologist faculty and 31.9% of radiology residents, with a wide range between institutions (19.5–47.8% for faculty and 13.3%–47.1% for residents). There was a strong positive correlation between the proportion of female faculty and the proportion of female residents within individual programs (r=0.73; R2=0.54; p=0.002). Conclusion: Approximately one third of faculty and residents at Canadian Diagnostic Radiology residency programs were female but there was a wide range across the country with a strong correlation between faculty and resident gender distribution. Further exploration is warranted to determine causes of this correlation including the possible influence of role modeling, mentoring, female-friendly culture, and bias.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".