Development, implementation, and evaluation of a competency-based didactic and simulation-focused boot camp for incoming urology residents: Report of the first three years
Bibliographic record
Abstract
INTRODUCTION: The Royal College of Physicians and Surgeons of Canada's Competence by Design (CBD) initiative presents curricula challenges to ensure residents gain proficiency while progressing through training. To prepare first-year urology residents (R1s), we developed, implemented, and evaluated a didactic and simulation-focused boot camp to implement the CBD curriculum. We report our experiences and findings of the first three years. METHODS: Urology residents from two Canadian universities participated in the two-day boot camp at the beginning of residency. Eleven didactic and six simulation sessions allowed for instruction and deliberate practice with feedback. Pre-and post-course multiple-choice questionnaires (MCQs) and an objective structured clinical exam (OSCE) evaluated knowledge and skills uptake. For initial program evaluation, three R2s served as historical controls in year 1. RESULTS: Nineteen residents completed boot camp. The mean age was 26.4 (±2.8) and 13 were male. Participants markedly improved on the pre- and post-MCQs (year 1: 62% and 91%; year 2: 55% and 89%; year 3: 58% and 86%, respectively). Participants scored marginally higher than the controls on four of the six OSCE stations. OSCE scores remained >88% over the three cohorts. All participants reported higher confidence levels post-boot camp and felt it was excellent preparation for residency. CONCLUSIONS: During its first three years, our urology boot camp has demonstrated high feasibility and utility. Knowledge and technical skills uptake were established via MCQ and OSCE results, with participants' scores near or above those of R2 controls. This boot camp will remain in our CBD curriculum and can provide a framework for other urology residency programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".