Simulation-based evaluation of anaesthesia residents: optimising resource use in a competency-based assessment framework
Bibliographic record
Abstract
Introduction: Simulation training in anaesthesiology bridges the gap between theory and practice by allowing trainees to engage in high-stakes clinical training without jeopardising patient safety. However, implementing simulation-based assessments within an academic programme is highly resource intensive, and the optimal number of scenarios and faculty required for accurate competency-based assessment remains to be determined. Using a generalisability study methodology, we examine the structure of simulation-based assessment in regard to the minimal number of scenarios and faculty assessors required for optimal competency-based assessments. Methods: Seventeen anaesthesiology residents each performed four simulations which were assessed by two expert raters. Generalisability analysis (G-analysis) was used to estimate the extent of variance attributable to (1) the scenarios, (2) the assessors and (3) the participants. The D-coefficient and the G-coefficient were used to determine accuracy targets and to predict the impact of adjusting the number of scenarios or faculty assessors. Results: We showed that multivariate G-analysis can be used to estimate the number of simulations and raters required to optimise assessment. In this study, the optimal balance was obtained when four scenarios were assessed by two simulation experts. Conclusion: Simulation-based assessment is becoming an increasingly important tool for assessing the competency of medical residents in conjunction with other assessment methods. G-analysis can be used to assist in planning for optimal resource use and cost-efficacy.
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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 | 0.064 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".