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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".