The Validity of Performance Assessments Using Simulation
Bibliographic record
Abstract
BACKGROUND: The authors wished to determine whether a simulator-based evaluation technique assessing clinical performance could demonstrate construct validity and determine the subjects' perception of realism of the evaluation process. METHODS: Research ethics board approval and informed consent were obtained. Subjects were 33 university-based anesthesiologists, 46 community-based anesthesiologists, 23 final-year anesthesiology residents, and 37 final-year medical students. The simulation involved patient evaluation, induction, and maintenance of anesthesia. Each problem was scored as follows: no response to the problem, score = 0; compensating intervention, score = 1; and corrective treatment, score = 2. Examples of problems included atelectasis, coronary ischemia, and hypothermia. After the simulation, participants rated the realism of their experience on a 10-point visual analog scale (VAS). RESULTS: After testing for internal consistency, a seven-item scenario remained. The mean proportion scoring correct answers (out of 7) for each group was as follows: university-based anesthesiologists = 0.53, community-based anesthesiologists = 0.38, residents = 0.54, and medical students = 0.15. The overall group differences were significant (P < 0.0001). The overall realism VAS score was 7.8. There was no relation between the simulator score and the realism VAS (R = -0.07, P = 0.41). CONCLUSIONS: The simulation-based evaluation method was able to discriminate between practice categories, demonstrating construct validity. Subjects rated the realism of the test scenario highly, suggesting that familiarity or comfort with the simulation environment had little or no effect on performance.
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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 | 0.027 | 0.141 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".