High-Fidelity Simulation in Pediatric Emergency Medicine
Bibliographic record
Abstract
OBJECTIVES: High-fidelity simulation (HFS) is widely used in pediatric emergency medicine (PEM) training and a competent facilitator is vital for effective learning. This survey describes the characteristics, comfort, practices, and need of PEM physicians as HFS facilitators. METHODS: A descriptive cross-sectional survey was electronically distributed to Pediatric Emergency Research Canada physician members, representing 14 academic pediatric emergency departments nationally. RESULTS: The response rate was 66.6% (92/138); 63% (56/89) of PEM physicians taught HFS. Junior attending physicians (P = 0.011) and those with an education focus (P = 0.005) were more comfortable in using HFS. Sixty-eight percent (38/56) described their facilitator training as formal. Generally, facilitators felt comfortable in running simulations (weighted mean scale, 1.53 [<2 = comfortable] on a 5-point rating scale). Facilitators with formal training used verbal confidentiality agreements more frequently (P = 0.008), spent less time running the scenario (P < 0.05) and spent more time in debriefing (P < 0.05) than those without formal training. Sixty-three percent (n = 56) of facilitators identified debriefing as the most stressful aspect of HFS. Their main barrier to HFS teaching was lack of protected teaching time (mean scale, 2.02 [>2 = barrier]). Seventy-six percent (35/46) of respondents desired online and printable facilitator information. Seventy percent (35/51) thought the ideal time for formal facilitator training was during fellowship. CONCLUSIONS: High-fidelity simulation is a widely used educational modality, and more attention must be paid to the needs of the facilitator in order to optimize the educational experience. Standardized facilitator training, focused particularly on effective debriefing techniques, would help improve facilitator comfort with HFS.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".