Empirical Investigations A Comparison of Global Rating Scale and Checklist Scores in the Validation of an Evaluation Tool to Assess Performance in the Resuscitation of Critically Ill Patients During Simulated Emergencies (Abbreviated as “CRM Simulator Stu
Bibliographic record
Abstract
Background: Crisis resource management (CRM) skills are a set of nonmedical skills required to manage medical emergencies. There is currently no gold standard for evaluation of CRM performance. A prior study examined the use of a global rating scale (GRS) to evaluate CRM performance. This current study compared the use of a GRS and a checklist as formal rating instruments to evaluate CRM performance during simulated emergencies. Methods: First-year and third-year residents participated in two simulator scenarios each. Three raters then evaluated resident performance in CRM using edited video recordings using both a GRS and a checklist. The Ottawa GRS provides a seven-point anchored ordinal scale for performance in five categories of CRM, and an overall performance score. The Ottawa CRM checklist provides 12 items in the five categories of CRM, with a maximum cumulative score of 30 points. Construct validity was measured on the basis of content validity, response process, internal structure, and response to other variables. T-test analysis of Ottawa GRS scores was conducted to examine response to the variable of level of training. Intraclass correlation coefficient
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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 | 0.092 | 0.265 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".