Development of an in situ simulation-based continuing professional development curriculum in pediatric emergency medicine
Bibliographic record
Abstract
BACKGROUND: Continuing professional development (CPD) activities delivered by simulation to independently practicing physicians are becoming increasingly popular. At present, the educational potential of such simulations is limited by the inability to create effective curricula for the CPD audience. In contrast to medical trainees, CPD activities lack pre-defined learning expectations and, instead, emphasize self-directed learning, which may not encompass true learning needs. We hypothesize that we could generate an interprofessional CPD simulation curriculum for practicing pediatric emergency medicine (PEM) physicians in a single-center tertiary care hospital using a deliberative approach combined with Kern's six-step method of curriculum development. METHODS: From a comprehensive core list of 94 possible PEM clinical presentations and procedures, we generated an 18-scenario CPD simulation curriculum. We conducted a comprehensive perceived and unperceived needs assessment on topics to include, incorporating opinions of faculty PEM physicians, hospital leadership, interprofessional colleagues, and expert opinion on patient benefit, simulation feasibility, and value of simulating the case for learning. To systematically rank items while balancing the needs of all stakeholders, we used a prioritization matrix to generate objective "priority scores." These scores were used by CPD planners to deliberately determine the simulation curriculum contents. RESULTS: We describe a novel three-step CPD simulation curriculum design method involving (1) systematic and deliberate needs assessment, (2) systematic prioritization, and (3) curriculum synthesis. Of practicing PEM physicians, 17/20 responded to the perceived learning needs survey, while 6/6 leaders responded to the unperceived needs assessment. These ranked data were input to a five-variable prioritization matrix generating priority scores. Based on local needs, the highest 18 scoring clinical presentations and procedures were selected for final inclusion in a PEM CPD simulation curriculum. An interim survey of PEM physician (21/24 respondents) opinions was collected, with 90% finding educational value with the curriculum. The curriculum includes items not identified by self-directed learning that PEM physicians thought should be included. CONCLUSIONS: We highlight a novel methodology for PEM physicians that can be adapted by other specialities when designing their own CPD simulation curriculum. This methodology objectively considers and prioritizes the needs of practicing physicians and stakeholders involved in CPD.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".