Comparison of Simulation‐based Resuscitation Performance Assessments With In‐training Evaluation Reports in Emergency Medicine Residents: A Canadian Multicenter Study
Bibliographic record
Abstract
OBJECTIVE: Simulation stands to serve an important role in modern competency-based programs of assessment in postgraduate medical education. Our objective was to compare the performance of individual emergency medicine (EM) residents in a simulation-based resuscitation objective structured clinical examination (OSCE) using the Queen's Simulation Assessment Tool (QSAT), with portfolio assessment of clinical encounters using a modified in-training evaluation report (ITER) to understand in greater detail the inferences that may be drawn from a simulation-based OSCE assessment. METHODS: A prospective observational study was employed to explore the use of a multicenter simulation-based OSCE for evaluation of resuscitation competence. EM residents from five Canadian academic sites participated in the OSCE. Video-recorded performances were scored by blinded raters using the scenario-specific QSATs with domain-specific anchored scores (primary assessment, diagnostic actions, therapeutic actions, communication) and a global assessment score (GAS). Residents' portfolios were evaluated using a modified ITER subdivided by CanMEDS roles (medical expert, communicator, collaborator, leader, health advocate, scholar, and professional) and a GAS. Correlational and regression analyses were performed comparing components of each of the assessment methods. RESULTS: Portfolio review and ITER scoring was performed for 79 residents participating in the simulation-based OSCE. There was a significant positive correlation between total OSCE and ITER scores (r = 0.341). The strongest correlations were found between ITER medical expert score and each of the OSCE GAS (r = 0.420), communication (r = 0.443), and therapeutic action (r = 0.484) domains. ITER medical expert was a significant predictor of OSCE total (p = 0.002). OSCE therapeutic action was a significant predictor of ITER total (p = 0.02). CONCLUSIONS: Simulation-based resuscitation OSCEs and portfolio assessment captured by ITERs appear to measure differing aspects of competence, with weak to moderate correlation between those measures of conceptually similar constructs. In a program of competency-based assessment of EM residents, a simulation-based OSCE using the QSAT shows promise as a tool for assessing medical expert and communicator roles.
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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 | 0.016 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".