Simulation vs workplace-based assessment in resuscitation: a cross-specialty descriptive analysis and comparison
Bibliographic record
Abstract
Background: Simulation-based assessment can complement workplace-based assessment of rare or difficult to assess Entrustable Professional Activities (EPAs). We aimed to compare the use of simulation-based assessment for resuscitation-focused EPAs in three postgraduate medical training programs and describe faculty perceptions of simulation-based assessment. Methods: EPA assessment scores and setting (simulation or workplace) were extracted from 2017-2020 for internal medicine, emergency medicine, and surgical foundations residents at the transition to discipline and foundations of discipline stages. A questionnaire was distributed to clinical competency committee members. Results: Eleven percent of EPA assessments were simulation-based. The proportion of simulation-based assessment did not differ between programs but differed between transition (38%) and foundations (4%) stages within surgical foundations only. Entrustment scores differed between settings in emergency medicine at the transition level only (simulation: 4.82 ± 0.60 workplace: 3.74 ± 0.93). 70% of committee members (n=20) completed the questionnaire. Of those that use simulation-based assessment, 45% interpret them differently than workplace-based assessments. 73% and 100% trust simulation for high-stakes and low-stakes assessment, respectively. Conclusions: The proportion of simulation-based assessment for resuscitation focused EPAs did not differ between three postgraduate medical training programs. Interpretation of simulation-based assessment data between committee members was inconsistent. All respondents trust simulation-based assessment for low-stakes, and the majority for high-stakes assessment. These findings have practical implications for the integration simulation into programs of assessment.
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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 | 0.022 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".