Simulation versus real-world performance: a direct comparison of emergency medicine resident resuscitation entrustment scoring
Bibliographic record
Abstract
BACKGROUND: Simulation is increasingly being used in postgraduate medical education as an opportunity for competency assessment. However, there is limited direct evidence that supports performance in the simulation lab as a surrogate of workplace-based clinical performance for non-procedural tasks such as resuscitation in the emergency department (ED). We sought to directly compare entrustment scoring of resident performance in the simulation environment to clinical performance in the ED. METHODS: The resuscitation assessment tool (RAT) was derived from the previously implemented and studied Queen's simulation assessment tool (QSAT) via a modified expert review process. The RAT uses an anchored global assessment scale to generate an entrustment score and narrative comments. Emergency medicine (EM) residents were assessed using the RAT on cases in simulation-based examinations and in the ED during resuscitation cases from July 2016 to June 2017. Resident mean entrustment scores were compared using Pearson's correlation coefficient to determine the relationship between entrustment in simulation cases and in the ED. Inductive thematic analysis of written commentary was conducted to compare workplace-based with simulation-based feedback. RESULTS: < 0.01). Further, qualitative analysis demonstrated overall management and leadership themes were more common narratives in the workplace, while more specific task-based feedback predominated in the simulation-based assessment. Both workplace-based and simulation-based narratives frequently commented on communication skills. CONCLUSIONS: In this single-center study with a limited sample size, assessment of residents using entrustment scoring in simulation settings was demonstrated to have a moderate positive correlation with assessment of resuscitation competence in the workplace. This study suggests that resuscitation performance in simulation settings may be an indicator of competence in the clinical setting. However, multiple factors contribute to this complicated and imperfect relationship. It is imperative to consider narrative comments in supporting the rationale for numerical entrustment scores in both settings and to include both simulation and workplace-based assessment in high-stakes decisions of progression.
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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 | 0.018 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".