Evaluation of medical students’ performance using the anaesthesia simulator
Bibliographic record
Abstract
OBJECTIVES: A pilot project assessing clinical performance was undertaken using the Anaesthesia Simulation Centre at the University of Toronto. The purpose of this study was to determine the reliability of assessments of medical students' performance using the simulator as an evaluation tool, to compare these assessments to written and clinical evaluations and to elicit student opinion. Simulator assessments were performed at the completion of the anaesthesia rotation. DESIGN: Twenty-four of 177 University of Toronto medical students participated in a videotaped simulator session with an attending faculty. These 24 students were based at Sunnybrook Health Science Centre. During the session, each student worked through one of six predetermined cases involving discrete patient problems based on the list of core objectives. Five evaluators independently assessed each student's videotaped performance using standardized performance evaluation criteria and data were examined for inter-rater reliability. Clinical and written examination marks were compared to simulator assessments. A student questionnaire was administered and descriptive data obtained. SETTING: The University of Toronto. SUBJECTS: Medical students. RESULTS: The intraclass correlation coefficient (ICC) of inter-rater reliability was 0.87. There was poor correlation between the simulator and written examination marks (r=0.19, P=0.38) and between the simulator and clinical marks (r=0.04, P=0.87). The simulator experience was highly rated by students: learning experience 4.6 +/- 0.51, appropriate content 4.4 +/- 0.74, use as evaluation tool, 4.1 +/- 0.92 (1=poor, 5=excellent). CONCLUSIONS: Our pilot data suggest that the simulator is a reliable assessment method for medical students' performance. Further work may justify the inclusion of the simulator as an evaluation and education tool and expanded to incorporate learning objectives of other medical disciplines.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".