Are battlefield and prehospital trauma scenarios an effective educational tool to teach leadership and crisis resource management skills to undergraduate medical students?
Bibliographic record
Abstract
INTRODUCTION: Leadership and crisis resource management (CRM) skills are important skills for doctors, however there is a recognised lack of undergraduate leadership education. There remains debate over how best to teach leadership and CRM skills, and poor leadership skills among clinicians are associated with adverse patient outcomes. We examined whether high-fidelity battlefield and prehospital scenarios can improve leadership and CRM skills. METHOD: This was a prospective observational study with students self-reporting their leadership and CRM skills using the Ottawa Crisis Resource Management Global Ranking Scale (OCRMGRS) before and after completing the Cambridge University Emergency Medicine Society Battlefield and Pre-Hospital Trauma course. The course involves a mixture of small group tutorials and practical high-fidelity battlefield and prehospital trauma scenarios. Faculty also completed the OCRMGRS for the first and last candidates at the scenarios. The mean precourse versus mean postcourse score of the OCRMGRS was analysed using a two-tailed t-test. RESULTS: 46 students completed paired OCRMGRS before and after the course. The mean precourse scores for each of the domains (leadership, communication skills, resource utilisation, problem solving skills and situational awareness) were calculated. There was a statistically significant (p<0.05) increase in both self-reported and faculty-reported scores across all domains, and the increase remained at 1-year follow-up. CONCLUSIONS: Leadership and CRM skills are important non-clinical skills for doctors, however there is debate over how best to teach them. High-fidelity battlefield and prehospital trauma scenarios are an effective means of teaching leadership and CRM skills to civilian medical students.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".