Adaptation of a Simulation Model and Checklist to Assess Pediatric Emergency Care Performance by Prehospital Teams
Bibliographic record
Abstract
INTRODUCTION: Simulation tools to assess prehospital team performance and identify patient safety events are lacking. We adapted a simulation model and checklist tool of individual paramedic performance to assess prehospital team performance and tested interrater reliability. METHODS: We used a modified Delphi process to adapt 3 simulation cases (cardiopulmonary arrest, seizure, asthma) and checklist to add remote physician direction, target infants, and evaluate teams of 2 paramedics and 1 physician. Team performance was assessed with a checklist of steps scored as complete/incomplete by raters using direct observation or video review. The composite performance score was the percentage of completed steps. Interrater percent agreement was compared with the original tool. The tool was modified, and raters trained in iterative rounds until composite performance scoring agreement was 0.80 or greater (scale <0.20 = poor; 0.21-0.39 = fair, 0.40-0.59 = moderate; 0.60-0.79 = good; 0.80-1.00 = very good). RESULTS: We achieved very good interrater agreement for scoring composite performance in 2 rounds using 6 prehospital teams and 4 raters. The original 175 step tool was modified to 171 steps. Interrater percent agreement for the final modified tool approximated the original tool for the composite checklist (0.80 vs. 0.85), cardiopulmonary arrest (0.82 vs. 0.86), and asthma cases (0.80 vs. 0.77) but was lower for the seizure case (0.76 vs. 0.91). Most checklist items (137/171, 80%) had good-very good agreement. Among 34 items with fair-moderate agreement, 15 (44%) related to patient assessment, 9 (26%) equipment use, 6 (18%) medication delivery, and 4 (12%) cardiopulmonary resuscitation quality. CONCLUSIONS: The modified checklist has very good agreement for assessing composite prehospital team performance and can be used to test effects of patient safety interventions.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".