Development and incorporation of hybrid simulation OSCE into in-training examinations to assess multiple CanMEDS competencies in urologic trainees
Bibliographic record
Abstract
INTRODUCTION: As residency training requirements increasingly emphasize a competency-based approach, novel tools to directly evaluate Canadian Medical Education Directives for Specialists (CanMEDS) competencies must be developed. Incorporating simulation allows residents to demonstrate knowledge and skills in a safe, standardized environment. We describe a novel hybrid simulation station for use in a urology resident in-training Objective Structured Clinical Exam (OSCE) to assess multiple CanMEDS competencies. METHODS: An OSCE station was developed to assess Communicator, Health Advocate, Manager, and Medical Expert (including technical skills) CanMEDS roles. Residents interviewed a standardized patient, interacted with a nurse, performed flexible cystoscopy and attempted stent removal using a novel bladder/stent model. Communication was assessed using the Calgary-Cambridge Observational Guide, knowledge was assessed using a checklist, and technical skills were assessed using a previously validated global rating scale. Video debriefing allowed residents to review their performance. Face and discriminative validity were assessed, and feasibility was determined through qualitative post-examination interviews and cost analysis. RESULTS: All 9 residents (postgraduate years [PGY] 3, 4, 5) completed the OSCE in 15 minutes. Communicator and knowledge scores were similar among all PGYs. Scores in technical skills were higher in PGY-5 compared with PGY-3/4 reside nts (mean score 79% vs. 73%). Residents and exam personnel felt the OSCE station allowed for realistic demonstration of competencies. Equipment cost was $218 for the exam station. CONCLUSIONS: We developed and implemented a hybrid simulation-based OSCE station to assess multiple CanMEDS roles. This approach was feasible and cost-effective; it also provided a framework for future development of similar OSCE stations to assess resident competencies across multiple domains.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".