Queen’s Simulation Assessment Tool
Bibliographic record
Abstract
INTRODUCTION: The use of high-fidelity simulation is emerging as an effective approach to competency-based assessment in medical education. We aimed to develop and validate a modifiable anchored global assessment scoring tool for simulation-based Objective Structured Clinical Examinations (OSCEs) of resuscitation competence in postgraduate emergency medicine (EM) trainees. METHODS: The Queen's Simulation Assessment Tool was developed using a modified Delphi technique with a panel of EM physicians. Ten standardized resuscitation OSCE scenarios were administered to EM trainees, and their video-recorded performances were scored by 3 independent and blinded EM attending physicians using the Queen's Simulation Assessment Tool. Correlational analyses and analysis of variance were applied to measure the discriminatory capabilities and interrater reliability of each scenario. A fully crossed generalizability study was conducted for each examination. RESULTS: Emergency medicine postgraduate trainees at Queen's University (n = 19-25 per station) participated in the study over 3 years. Interrater reliability showed acceptable levels of agreement for each scenario (mean Spearman ρ = 0.75 [0.63-0.87]; mean interclass correlation coefficient, 0.69 [0.58-0.87]). Discriminatory validity was strong, with senior residents outperforming junior residents in all but 1 of the 10 scenarios. Generalizability studies found the trainee and trainee by scenario interactions as the largest contributors to variance, with G coefficients ranging from 0.67 to 0.84. Resident trainees reported comfort being assessed in the simulation environment (3.8/5) and found the simulation-based examination valuable to their learning (4.6/5). CONCLUSIONS: This study describes the development and validation of a novel modifiable anchored global assessment scoring tool for simulation-based OSCE assessment of resuscitation competence in postgraduate EM 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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.009 |
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".