Evaluation of Canadian urology residency and fellowship program websites
Bibliographic record
Abstract
Introduction: There is growing use of online resources in the postgraduate medical education application process to provide applicants program-specific details, thus allowing for informed decision-making. Given the variability and non-standardized electronic training descriptions and objectives, our goal was to assess the availability of program information through program websites for both residency and fellowship urology programs across Canada. Methods: Using the Canadian Residency Matching Service (CaRMS) and the Canadian Urological Association (CUA) websites, we compiled a list of all Canadian urology residency and fellowship programs. We reviewed all programs’ website using a 40-item tool based on seven subcategories, including education, application process, faculty information, trainee/fellow information, research and extra-curricular activities, wellness, and both benefits and career planning. Each website was reviewed by two trained reviewers. Any inter-reviewer discrepancy was resolved by a third-party reviewer. Results: Among 13 Canadian urology residency programs, all had program websites and met 48% of the criteria evaluated. None of the residency program websites reported information on work hours, surgical caseload statistics, or equity diversity and inclusion/community initiatives. Among 37 Canadian urology fellowship programs, 10 programs did not have websites, and the remaining 27 program websites met 28% of the criteria evaluated. Scores were highest for the application process subcategory, while scores were lowest for the wellness and benefits/career planning subcategories among both residency and fellowship programs. Conclusions: With growing reliance and dependence on web resources to access residency and fellowship program information, there is a clear need to standardize and improve Canadian training websites for prospective applicants.
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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 | 0.034 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".