Building a Simulation‐based Crisis Resource Management Course for Emergency Medicine, Phase 1: Results from an Interdisciplinary Needs Assessment Survey
Bibliographic record
Abstract
INTRODUCTION: Emergency department (ED) resuscitation requires the coordinated efforts of an interdisciplinary team. Human errors are common and have a negative impact on patient safety. Although crisis resource management (CRM) skills are utilized in other clinical domains, most emergency medicine (EM) caregivers currently receive no formal CRM training. OBJECTIVES: The objectives were to compile and compare attitudes toward CRM training among EM staff physicians, nurses, and residents at two Canadian academic teaching hospitals. METHODS: Emergency physicians (EPs), residents, and nurses were asked to complete a Web survey that included Likert scales and short answer questions. Focus groups and pilot testing were used to inform survey development. Thematic content analysis was performed on the qualitative data set and compared to quantitative results. RESULTS: The response rate was 75.7% (N = 84). There was strong consensus regarding the importance of core CRM principles (i.e., effective communication, team leadership, resource utilization, problem-solving, situational awareness) in ED resuscitation. Problems with coordinating team actions (58.8%), communication (69.6%), and establishing priorities (41.3%) were among factors implicated in adverse events. Interdisciplinary collaboration (95.1%), efficiency of patient care (83.9%), and decreased medical error (82.6%) were proposed benefits of CRM training. Communication between disciplines is a barrier to effective ED resuscitation for 94.4% of nurses and 59.7% of EPs (p = 0.008). Residents reported a lack of exposure to (64.3%), yet had interest in (96.4%) formal CRM education using human patient simulation. CONCLUSIONS: Nurses rate communication as a barrier to teamwork more frequently than physicians. EM residents are keen to learn CRM skills. An opportunity exists to create a novel interdisciplinary CRM curriculum to improve EM team performance and mitigate human error.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".