PEDIATRIC SIMULATION IN EMERGENCY MEDICINE RESIDENCY TRAINING
Bibliographic record
Abstract
BACKGROUND: Simulation based training is correlated with improved outcomes in pediatrics and as an educational modality, has been widely adopted in pediatric emergency medicine. This is especially important given the infrequent nature of high acuity events in pediatrics, even amongst high volume centers. However, the majority of children are still cared for outside of pediatric centers. Because general emergency departments see large numbers of children, it is important that pediatric training be robust in emergency medicine training. Prior research has shown that there is a positive correlation between simulation based learning programs with improved pediatric outcomes. While most emergency medicine programs in Canada have integrated simulation into their programs, it remains unclear how pediatric specific simulation has been incorporated. OBJECTIVES 1. Determine if and how pediatric simulation is formally integrated into existing simulation curricula in Emergency Medicine training programs. 2. Determine barriers of implementation of pediatric simulation. DESIGN/METHODS: An anonymous cross-sectional survey was sent to program directors of Royal College accredited emergency medicine programs across Canada. RESULTS CONCLUSION: The survey shows that all programs report formal integration of pediatric simulation into their residency curricula. Most programs use pediatric simulation to practice high stakes/rare events and report needs in neonatal and pediatric resuscitation in particular. Use of simulation as an evaluative tool remains uncommon. The most significant barriers in implementing simulation relate to human resources. Programs appear reluctant to use nationally developed curricula.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".