Perceptions on Competence by Design in urology
Bibliographic record
Abstract
INTRODUCTION: The Royal College of Physicians and Surgeons of Canada has begun implementing Competence by Design (CBD). However, it is unclear how much urology trainees and faculty know about CBD, their attitudes towards this change, and their willingness to embrace and participate in this new model of training. METHODS: This cross-sectional study was conducted through an online survey, which was administered to all trainees and faculty at Canadian urology programs prior to the implementation of CBD. The final survey consisted of eight demographic questions, 17 five-point Likert items, one visual analog scale question, 11 multiple selection questions, and two open-ended questions. RESULTS: A total of 74 participants (38 faculty and 36 trainees) across 12 universities responded, with a completion rate of 82.4%. This corresponded to an overall response rate of 20.5%. Overall, there was a lack of resounding enthusiasm towards this shift to CBD in urology. Although both trainees and faculty had overall positive perceptions of CBD on assessment, teaching, and readiness, most agreed that this transition will be costly and associated with increased requirements for time, funding, and administrative support. Furthermore, there were significant concerns regarding the lack of valid assessment tools and evidence for the validity of entrustable professional activities. CONCLUSIONS: While this survey has demonstrated an appreciation for the benefits of CBD, challenges are equally anticipated. CBD in urology will be a fertile research area; this study has identified several important educational questions regarding the model's effectiveness and consequences, thus, providing collaborative opportunities among all Canadian programs.
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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 | 0.025 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".