A Comparison of Global Ratings and Checklist Scores from an Undergraduate Assessment Using an Anesthesia Simulator
Bibliographic record
Abstract
PURPOSE: To determine the correlation between global ratings and criterion-based checklist scores, and inter-rater reliability of global ratings and criterion-based checklist scores, in a performance assessment using an anesthesia simulator. METHOD: All final-year medical students at the University of Toronto were invited to work through a 15-minute faculty-facilitated scenario using an anesthesia simulator. Students' performances were videotaped and analyzed by two faculty using a 25-point criterion-based checklist and a five-point global rating of competency (1 = clear failure, 5 = superior performance). Correlations between global ratings and checklist scores, as well as specific performance competencies (knowledge, technical skills, and judgment), were determined. Checklist and global scores were converted to percentages; means of the two marks were compared. Mean reliability of a single rater for both checklist and global ratings was determined. RESULTS: The correlation between checklist and global ratings was.74. Mean ratings of both checklist and global scores were low (58.67, SD = 14.96, and 57.08, SD = 24.27, respectively); these differences were not statistically significant. For a single rater, the mean reliability score across rater pairs for checklist scores was.77 (range.58-.93). Mean reliability score across rater pairs for global ratings was.62 (.40-.77). Global ratings correlated more highly with technical skills and judgment (r =.51 and r =.53, respectively) than with knowledge. (r =.24) CONCLUSION: Inter-rater reliability was higher for checklist scores than for global ratings; however, global ratings demonstrated acceptable inter-rater reliability and may be useful for competency assessment in performance assessments using simulators.
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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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".