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 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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.019 | 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".