Pitfalls in Emergency Medicine: Survey-Based Identification of Learning Objectives for Targeted Simulation Curricula by Emergency Department Staff
Bibliographic record
Abstract
INTRODUCTION: The emergency department is a complex practice environment into which numerous factors may introduce both human and system error. Emergency physicians have to assemble and manage multidisciplinary teams with a moment's notice to manage critically ill patients. The EM training programs across Canada are diverse with considerable variation among programs. Acquisition of both high acuity low occurrence (HALO) and crisis resource management (CRM) skills are crucial to the development of proficient emergency room physicians. Physicians and allied health workers were surveyed to identify potential causes of error in local emergency departments and to find simulation-driven solutions. METHODS: An anonymous survey was prepared to evaluate potential pitfalls of emergency care in St. John's, NL, Canada. It was distributed electronically to 108 medical staff, including physicians, nurses, and postgraduate year three (PGY3) residents. Respondents were asked about their experience with simulation education, and whether or not they feel that there is an opportunity for it in postgraduate emergency medicine training. RESULTS: The response rate was 30%. Communication - with the emergency department team, consulting services, and patients - was identified as a potential topic for simulation, along with interruptions. Burnout, busy department, departmental crowding, end of shift handover, and incomplete/missing patient medical history were identified as topics that should be included in the emergency medicine curriculum. Following a review with the simulation expert panel, it was determined that end of shift handover could also be incorporated as a simulation in the existing curriculum. DISCUSSION: This survey looked at pitfalls in emergency medicine through a CRM lens. Six pitfalls were identified as important for patient safety, but not best addressed with simulation. These could be incorporated into the half-day curriculum as didactic lectures. Four important patient safety pitfalls were identified that could potentially be addressed with simulation and incorporated in the existing emergency medicine simulation curriculum.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".