A Simulation-Based Acute Care Curriculum for Pediatric Emergency Medicine Fellowship Training Programs
Bibliographic record
Abstract
OBJECTIVES: Currently, many pediatric hospitals are using simulation technology to teach trainees the skills required to effectively succeed in managing critically ill patients. Unfortunately, no curricula integrating the use of simulation have been described for pediatric emergency medicine (PEM) fellowship programs. Our objective was to outline our experience with the development, integration, and evaluation of a simulation-based, acute care curriculum into our current PEM fellowship training program. METHODS: Using the American Board of Pediatrics and the Royal College of Physicians and Surgeons of Canada learning objectives for PEM as a guide, 12 modules composed of 43 scenarios were developed to address the skill sets required for PEM fellows. Six modules were identified as "core," allocated for completion in year 1 of fellowship, whereas the remaining modules were "subspecialty," designed for completion in year 2 of training. A 12-question survey (5-point Likert scale) was used to evaluate trainee satisfaction with regard to 4 domains: level of realism, utility of debriefing, quality of instruction, and overall satisfaction. RESULTS: A total of 66 surveys were collected between March and July 2007. Twenty-five surveys were completed by PEM fellows. Trainees responded favorably for all 4 domains, reporting that the new simulation curriculum provided realistic scenarios with high-quality debriefing, instruction, and an overall excellent learning experience. CONCLUSIONS: We have successfully integrated a simulation-based acute care curriculum into our PEM fellowship program. Satisfaction ratings were high for this program. Research to assess educational outcomes related to this curriculum is necessary.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".