ArticleCureus2024
Localizing and Tailoring the Debriefing Assessment for Simulation in Healthcare to Optimize Fit.
Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction Debriefing in healthcare simulation is helpful in reinforcing learning objectives, closing performance gaps, and improving future practice and patient care. The Debriefing Assessment for Simulation in Healthcare (DASH) is a validated tool. However, localized rater training for the DASH has not been described. We sought to augment DASH anchors with localized notations, localize DASH rater training, assess localized DASH correlation with other debriefing practices/factors, and assess reliability of the localized tool. Methods This study was conducted at SimTiki Simulation Center, John A. Burns School of Medicine, University of Hawai'i at Mānoa. Three simulation experts without DASH training developed a list of debriefing best practices/factors, reviewed the DASH handbook, and transcribed the DASH Rater Long Form version with example behaviors into a rating document. Research assistants recorded best practices/factor data from archived debriefing videos. Simulation experts independently scored debriefings, resolved discrepancies and added localized criteria to the DASH. Rater calibration was completed with an intraclass correlation (ICC) of 0.884. Raters then independently scored 43 debriefings recorded during July-December 2022. DASH scores were compared to observed debriefing best practices/factors. Results The overall DASH behaviors ICC agreement was 0.810 and consistency was 0.825. Behavior scores ranged from 2.45 (SD 0.70) to 4.42 (SD 0.81). The three lowest scoring DASH behaviors were 2A (2.45), 4A (3.41), and 3B (3.44). In behavior 2B regarding realism concerns, there was significant inconsistency in the use of the not applicable (NA) designation. DASH and best practices/factors construct correlation supported convergent validity. Conclusion High interrater reliability followed localized rater training and the addition of localized notations to DASH. Correlation with debriefing practices/factors strengthens DASH validity evidence. Lower DASH behavior scores reported generally suggest that the interpretation of DASH scores is best contextualized in a shared localized DASH construct. A comparison of DASH numerical scores between institutions, with different raters, and different cultures may not reflect absolute debriefing quality.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.