A debriefing tool to acquire non-technical skills in trauma courses
Bibliographic record
Abstract
Objective: The study reports the use of a nominal group technique (NGT) to evaluate the PEARLS Healthcare debriefing tool as a tool to foster non-technical skills in trauma simulation courses. Additionally, it introduces a debriefing card to be used in trauma courses. Design: A nominal group technique was used to evaluate the main strategies for PEARLS. The experts had the opportunity to share their opinions in an online survey and online meeting. Results: Seven participants participated in the nominal group. Based on the online survey results, the self-assessment debriefing strategy (from PEARLS) was rated 4.83/5 in relevance, the focused facilitation 5/5, and the provision of information 4.5/5. Participants felt that PEARLS was appropriate and useful for fostering non-technical skills: all the debriefing strategies contained in PEARLS were felt to be valid and worth using; and cue cards for the instructors were suggested to assist them in conducting structured formal debriefings. A specific debriefing tool for trauma scenarios was designed based on these suggestions, which is presented in this article. Conclusion: A nominal group of experts in education, simulation, and trauma support PEARLS strategies for non-technical skills training in trauma courses.
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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 | 0.052 | 0.177 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".