Stakeholder perspectives and status of surgical simulation and skills training in urology residency programs in Canada
Bibliographic record
Abstract
Introduction: With the shift to competency-based training, surgical skills lab training (SSLT) may become a mandatory part of Canadian urology residency programs (CURPs). This study aims to identify: 1) the status of SSLT in CURP; 2) stakeholder perspectives on the utility of SSLT; 3) barriers to developing and implementing SSLT; and 4) how to address these barriers. Methods: Surveys were developed and issued to three groups of stakeholders: 1) SSLT directors at all 13 CURPs (response rate 100%); 2) teaching faculty (response rate 33%); and 3) urology residents (response rate 24%). Surveys 2 and 3 were sent to ten English CURP. Results were collected through email and SurveyMonkey®. Results: Nine of 13 CURPs have a dedicated SSLT; 46% of CURP have 1–3 sessions per year, 8% have 5–7, and 30% >7. Most residents have independent lab access, but 80% do so less than once monthly. Over 90% of stakeholders find SSLT useful, of which high-fidelity models are most preferred (faculty rated 3.66/4, residents 3.18/4). Program directors (PDs) identified lack of protected faculty time, funding, and infrastructure as the top three barriers to SSLT implementation. Residents found lack of faculty time, protected academic time, and infrastructure as barriers. PDs viewed protecting faculty time and more funding as potential solutions, while residents suggested protected faculty and academic time, and after-hours lab access. Conclusions: Residents, faculty, and PDs in CURPs view SSLT as useful. Most CURPs have defined SSLT; programs without this have labs for resident use but are underused. To continue to develop and progress SSLT, more time, participation, and funding must be made available.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".