The Role of High-Fidelity Simulation in Training Pediatric Emergency Medicine Fellows in the United States and Canada
Bibliographic record
Abstract
OBJECTIVES: The American Academy of Pediatrics Section on Emergency Medicine's Simulation Interest Group developed a survey targeting pediatric emergency medicine (PEM) fellowship program directors to assess the use of high-fidelity simulation (HFS) in PEM fellow training. METHODS: Content experts in simulation and in PEM developed a 38-item Internet-based questionnaire that was distributed to PEM program directors via e-mail though www.surveymonkey.com. RESULTS: Seventy-seven percent (51/66) of PEM program directors in the United States and Canada responded to the survey. Sixty-three percent of programs incorporate HFS in PEM fellowship training. For programs with HFS, the most frequent uses of HFS include (1) decision making for trauma resuscitations (97%, 31/32) and medical emergencies (91%, 29/32), and for the application of advanced life support (84%, 27/32); (2) technical skills: intubation (100%, 31/31), bag-mask ventilation (94%, 29/31), cardioversion/defibrillation (90%, 28/31), and difficult airway management (84%, 26/31). Of program directors without simulation, a majority valued simulation for PEM fellow training, and 59% (11/19) plan on expanding efforts. Perceived barriers to an active simulation program exist: lack of financial support (79%, 15/19), lack of simulator equipment (74%, 14/19), lack of a dedicated physical space (68%, 13/19), and insufficiently experienced simulation faculty (58% 11/19). CONCLUSIONS: Sixty-three percent of PEM fellowship programs integrate HFS-based activities. The majority of PEM fellowship program directors value the role of HFS in augmenting clinical experience and documenting procedural skills. Regional simulation centers are one possible solution to offer HFS training to fellowships with limited financial support and/or lack of experienced simulation faculty.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".