Improving Pediatric Trauma Education by Teaching Non-technical Skills: A Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Pediatric trauma is a significant cause of child mortality, and the absence of non-technical skills (NTS) among health providers is linked with errors in patients' care. In this study, we evaluate the effectiveness of a structured debriefing protocol in enhancing NTS during pediatric trauma simulation. METHODS: A total of 45 medical students were successfully recruited from two medical schools, one in Brazil and one in Canada. Medical students were assigned to a control (N = 20) or intervention group (N = 25) in a randomized control trial. Following simulated scenarios, participants in the intervention group underwent NTS debriefing, while the control received standard debriefing based on the Advanced Trauma Life Support (ATLS) protocol. Students' confidence, NTS level, and performance were measured through self-assessment surveys, the Non-Technical Skills for Surgeons (NOTSS) score, and adherence to the trauma protocol, respectively. Baseline characteristics and outcomes were compared using t-tests, Mann-Whitney, Wilcoxon signed-rank Kruskal-Wallis, ANOVA, and a repeated-measures ANCOVA. A significance level was set at p < 0.05. RESULTS: The workshop increased students' confidence in leading trauma resuscitation regardless of their assignment to condition. While controlling for covariates, students in the intervention group significantly improved their overall NOTSS compared to those in the control and in all categories: situational awareness, decision-making, communication and teamwork, and leadership. The intervention teams also demonstrated a significant increase in completing trauma protocol steps. CONCLUSION: Implementing structured debriefing focusing on NTS enhanced these skills and improved adherence to protocol among medical students managing pediatric trauma-simulated scenarios. These findings support integrating NTS training in pediatric trauma education. LEVEL OF EVIDENCE: I.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".