Effect of Repetitive Simulation Training on Emergency Medical Services Team Performance in Simulated Pediatric Medical Emergencies
Bibliographic record
Abstract
Abstract Objective Emergency medical services (EMS) professionals infrequently transport children leading to difficulty in recognition and management of pediatric critical illness. Simulation provides an opportunity to train EMS professionals on pediatric emergencies. The objective of this study was to examine the effect of serial simulation training over 6 months on EMS psychomotor and cognitive performance during team‐based care. Methods This was a longitudinal prospective study of a simulation curriculum enrolling EMS professionals over a 6‐month period during which they performed three high‐fidelity simulations at 3‐month intervals. The simulation scenarios included a 15‐month‐old seizure (T0), 1‐month‐old with hypoglycemia (T1), and 4‐year‐old clonidine ingestion (T2). All scenarios were standardized and required recognition and management of respiratory failure and decompensated shock. Scenarios were videotaped and two investigators scored EMS team interventions during simulations using a standardized scoring tool. Inter‐rater reliability was assessed on 30% of videos using kappa analysis. Volumes of administered intravenous fluid (IVF) and medications were measured to assess for errors in administration. The primary outcome was the change in scenario score from T0 to T2. Results A total of 135 team‐based simulations were conducted over the study period (48, 40, and 47 at T0, T1, and T2, respectively). Inter‐rater reliability between reviewers was very good (κ = 0.7). Median simulation score improved from T0 to T2 (24 vs 31, p < 0.001, maximum score possible = 42). The proportion of completed tasks increased across multiple categories including improved recognition of respiratory decompensation (19% vs. 56%), management of the pediatric airway (44% vs. 88%), and timeliness of vascular access (10% vs. 38%). Correct IVF administration varied by scenario (25% vs. 52% vs. 30%, p = 0.02). Conclusion Serial simulation improved EMS team‐based care in both recognition and management of pediatric emergencies. A standardized pediatric simulation curriculum can be used to train EMS professionals on pediatric emergencies and improve performance.
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.
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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".