Debriefing Assessment for Simulation in Healthcare
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
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.
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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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