Competency-based simulation assessment of resuscitation skills in emergency medicine postgraduate trainees – a Canadian multi-centred study
Bibliographic record
Abstract
BACKGROUND: The use of high-fidelity simulation is emerging as a desirable method for competency-based assessment in postgraduate medical education. We aimed to demonstrate the feasibility and validity of a multi-centre simulation-based Objective Structured Clinical Examination (OSCE) of resuscitation competence with Canadian Emergency Medicine (EM) trainees. METHOD: EM postgraduate trainees (n=98) from five Canadian academic centres participated in a high fidelity, 3-station simulation-based OSCE. Expert panels of three emergency physicians evaluated trainee performances at each centre using the Queen's Simulation Assessment Tool (QSAT). Intraclass correlation coefficients were used to measure the inter-rater reliability, and analysis of variance was used to measure the discriminatory validity of each scenario. A fully crossed generalizability study was also conducted for each examination centre. RESULTS: Inter-rater reliability in four of the five centres was strong with a median absolute intraclass correlation coefficient (ICC) across centres and scenarios of 0.89 [0.65-0.97]. Discriminatory validity was also strong (p < 0.001 for scenarios 1 and 3; p < 0.05 for scenario 2). Generalizability studies found significant variations at two of the study centres. CONCLUSIONS: This study demonstrates the successful pilot administration of a multi-centre, 3-station simulation-based OSCE for the assessment of resuscitation competence in post-graduate Emergency Medicine trainees.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".