Identifying and Transmitting the Culture of Emergency Medicine Through Simulation
Bibliographic record
Abstract
BACKGROUND: Simulation is commonly used in medical education. It offers the opportunity for participants to apply theoretical knowledge and practice nontechnical skills. We aimed to examine how simulation may also help to identify emergency medicine culture and serve as a tool to transmit values, beliefs, and practices to medical learners. METHODS: We undertook a focused ethnography of a simulated emergency department exercise delivered to 98 third-year medical students. This ethnography included participant observation, informal interviews, and document review. Analysis was performed using a recursive method, a simultaneous deductive and inductive approach to data interpretation. RESULTS: All 20 staff (100%) and 92 of 98 medical students (94%) participated in the study. We identified seven core values-identifying and treating dangerous pathology, managing uncertainty, patients and families at the center of care, balancing needs and resources at the system level, value of the team approach, education as integral, and emergency medicine as part of self-identity-and 27 related beliefs that characterized emergency medicine culture. We observed that culture was transmitted during the simulation exercise. CONCLUSION: This study contributes to the characterization of the culture of emergency medicine by identifying core values and beliefs that are foundational to the specialty. Simulation facilitated cultural compression, which allowed for ready identification of values, beliefs, and practices and also facilitated transmission of culture to learners. This study expands understanding of the culture of emergency medicine and the role of simulation in the process of cultural exchange.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 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".