Faculty Sim: Implementation of an Innovative, Simulation‐based Continuing Professional Development Curriculum for Academic Emergency Physicians
Bibliographic record
Abstract
BACKGROUND: Maintaining and enhancing competence in the breadth of emergency medicine (EM) is an ongoing challenge. In particular, resuscitative care in EM involves high-risk clinical encounters that demand strong procedural skills, effective team leadership, and up-to-date clinical knowledge. Simulation-based medical education is an effective modality for enhancing technical and nontechnical skills in crisis situations and has been effectively embedded in undergraduate and postgraduate medical curricula worldwide. To our knowledge, there are few existing systematic department-wide simulation programs to address continuing professional development (CPD) for practicing academic EM faculty. DEVELOPMENT PROCESS: We developed our novel, simulation-based CPD program following Kern's six-step model. Based on the results of a multimodal needs assessment, a longitudinal curriculum was mapped and tailored to the available resources. Institutional support was provided in the form of a departmental grant to fund a physician program lead, monthly session instructors, and operating costs. OUTCOMES: CPD simulation sessions commenced in January 2017. Our needs assessment identified two key types of educational needs: 1) crisis resource management skills and 2) frequent practice of high-stakes critical care procedures (e.g., surgical airways). Simulation sessions involve two high-fidelity simulated resuscitations and one skills lab per day. To date, 21 sessions have been delivered, reaching 161 practicing EPs. Feedback from our faculty has been positive. REFLECTIVE DISCUSSION: We have successfully introduced a curriculum of monthly simulation-based CPD based on the educational needs of our EPs. Future work will include more detailed program evaluation linked to clinical outcomes and program expansion to support nearby institutions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".