Simulation in the Continuing Professional Development of Academic Emergency Physicians
Bibliographic record
Abstract
INTRODUCTION: Simulation is becoming a popular educational modality for physician continuing professional development (CPD). This study sought to characterize how simulation-based CPD (SBCPD) is being used in Canada and what academic emergency physicians (AEPs) desire in an SBCPD program. METHODS: Two national surveys were conducted from March to June 2018. First, the SBCPD Needs Assessment Survey was administered online to all full-time AEPs across 9 Canadian academic emergency medicine (EM) sites. Second, the SBCPD Status Survey was administered by telephone to the department representatives (DRs)-simulation directors or equivalent-at 20 Canadian academic EM sites. RESULTS: Response rates for the SBCPD Needs Assessment and the SBCPD Status Survey were 40% (252/635) and 100% (20/20) respectively. Sixty percent of Canadian academic EM sites reported using SBCPD, although only 30% reported dedicated funding support. Academic emergency physician responses demonstrated a median annual SBCPD of 3 hours. Reported incentivization for SBCPD participation varied with AEPs reporting less incentivization than DRs. Academic emergency physicians identified time commitments outside of shift, lack of opportunities, and lack of departmental funding as their top barriers to participation, whereas DRs thought AEPs fear of peer judgment and inexperience with simulation were substantial barriers. Content areas of interest for SBCPD were as follows: rare procedures, pediatric resuscitation, and neonatal resuscitation. Lastly, interprofessional involvement in SBCPD was valued by both DRs and AEPs. CONCLUSIONS: Simulation-based CPD programs are becoming common in Canadian academic EM sites. Our findings will guide program coordinators in addressing barriers to participation, selecting content, and determining the frequency of SBCPD events.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".