Development and implementation of a novel, mandatory competency-based medical education simulation program for pediatric emergency medicine faculty
Bibliographic record
Abstract
BACKGROUND: Maintaining acute care physician competence is critically important. Current maintenance of certification (MOC) programs has started to incorporate simulation-based education (SBE). However, competency expectations have not been defined. This article describes the development of a mandatory annual SBE, competency-based simulation program for technical and resuscitation skills for pediatric emergency medicine (PEM) physicians. METHODS: The competency-based medical education (CBME) program was introduced in 2016. Procedural skill requirements were based on a needs assessment derived from Royal College PEM training guidelines. Resuscitation scenarios were modified versions of pre-existing in-situ mock codes or critical incident cases. All full-time faculty were required to participate annually in both sessions. Delivery of educational content included a flipped classroom website, deliberate practice, and stop-pause debriefing. All stations required competency checklists and global rating scales. RESULTS: Between 2016 and 2018, 40 physicians and 48 registered nurses attended these courses. Overall course evaluations in 2018 were 4.92/5 and 4.93/5. Barriers to implementation include the need for many simulation education experts, time commitment, and clinical scheduling during course events. CONCLUSION: We have developed a mandatory simulation-based, technical, and resuscitation CBME program for PEM faculty. This simulation-based CBME program could be adapted to other acute care disciplines. Further research is required to determine if these skills are enhanced both in a simulated and real environment and if there is an impact on patient outcomes.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".