Debriefing Assessment for Simulation in Healthcare
Bibliographic record
Abstract
INTRODUCTION: This study examined the reliability of the scores of an assessment instrument, the Debriefing Assessment for Simulation in Healthcare (DASH), in evaluating the quality of health care simulation debriefings. The secondary objective was to evaluate whether the instrument's scores demonstrate evidence of validity. METHODS: Two aspects of reliability were examined, interrater reliability and internal consistency. To assess interrater reliability, intraclass correlations were calculated for 114 simulation instructors enrolled in webinar training courses in the use of the DASH. The instructors reviewed a series of 3 standardized debriefing sessions. To assess internal consistency, Cronbach α was calculated for this cohort. Finally, 1 measure of validity was examined by comparing the scores across 3 debriefings of different quality. RESULTS: Intraclass correlation coefficients for the individual elements were predominantly greater than 0.6. The overall intraclass correlation coefficient for the combined elements was 0.74. Cronbach α was 0.89 across the webinar raters. There were statistically significant differences among the ratings for the 3 standardized debriefings (P < 0.001). CONCLUSIONS: The DASH scores showed evidence of good reliability and preliminary evidence of validity. Additional work will be needed to assess the generalizability of the DASH based on the psychometrics of DASH data from other settings.
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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 | 0.025 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".