The Validity of Performance Assessments Using Simulation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it