Trends in the training of female urology residents in Canada
Bibliographic record
Abstract
INTRODUCTION: There is limited research on why women do or do not choose a career in urology. Considering the increasing proportion of female medical students, we assessed for trends in female applicants to urology programs in Canada and their post-residency career choices. METHODS: Data from the Canadian Residency Matching Service (CaRMS) was used (1998-2015). Trends in the proportions of females applying and matching to surgical subspecialties, and applying and matching to urology were computed. Surveys were sent to urology program directors to assess female residents' chosen career paths over the last decade. RESULTS: A significant increasing trend in the proportion of females applying to urology as their first choice program was found (0.19 in 1998-99 to 0.27 in 2012-15; p=0.04). An increasing trend in the proportion of females successfully matching to urology was found, although it was not statistically significant (0.13 in 1998-99 to 0.24 in 2012-15; p=0.07). This was in keeping with the trends found for surgical programs overall. Female graduates choose a variety of career paths, with urogynecology being the most common fellowship (26%). CONCLUSIONS: The last two decades has seen an increase in the proportion of female students applying to urology in Canada. Female urology graduates pursue a variety of career paths. It remains imperative that both female and male medical students have early exposure and education about our subspecialty to ensure we continue to recruit the most talented candidates.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".