Impact and effectiveness of a mandatory competency‐based simulation program for pediatric emergency medicine faculty
Bibliographic record
Abstract
Introduction: Pediatric emergency medicine physicians struggle to maintain their critical procedural and resuscitation skills. Continuing professional development programs incorporating simulation and competency-based standards may help ensure skill maintenance. Using a logic model framework, we sought to evaluate the effectiveness of a mandatory annual competency-based medical education (CBME) simulation program. Methods: The CBME program, evaluated from 2016 to 2018, targeted procedural, point-of-care ultrasound (POCUS) and resuscitation skills. Delivery of educational content included a flipped-classroom website, deliberate practice, mastery-based learning, and stop-pause debriefing. Participants' competence was assessed using a 5-point global rating scale (GRS; 3 = competent, 5 = mastery). Statistical process control charts were used to measure the effect of the CBME program on team performance during in situ simulations (ISS), measured using the Team Emergency Assessment Measure (TEAM) scale. Faculty completed an online program evaluation survey. Results: Forty physicians and 48 registered nurses completed at least one course over 3 years (physician mean ± SD 2.2 ± 0.92). Physicians achieved competence on 430 of 442 stations (97.3%). Mean ± SD GRS scores for procedural, POCUS, and resuscitation stations were 4.34 ± 0.43, 3.96 ± 0.35, and 4.17 ± 0.27, respectively. ISS TEAM scores for "followed standards and guidelines" improved significantly. No signals of special cause variation emerged for the other 11 TEAM items, indicating skills maintenance. Physicians rated CBME training as highly valuable (mean question scores 4.15-4.85/5). Time commitment and scheduling were identified as barriers to participation. Conclusions: Our mandatory simulation-based CBME program had high completion rates and very low station failures. The program was highly rated and faculty improved or maintained their ISS performance across TEAM scale domains.
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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 | 0.013 |
| 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 | 0.001 | 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".