Evaluating Best Methods for Crisis Resource Management Education
Bibliographic record
Abstract
INTRODUCTION: Health care training traditionally focuses on medical knowledge; however, this is not the only component of successful patient management. Nontechnical skills, such as crisis resource management (CRM), have significant impact on patient care. This study examines whether there is a difference in CRM skills taught by traditional lecture in comparison with low-fidelity simulation consisting of noncontextual learning through team problem-solving activities. METHODS: Two groups of multidisciplinary preclinical students were taught CRM through lecture or noncontextual active learning. Both groups were given a cardiopulmonary resuscitation simulation and clinical performance assessed by basic life support (BLS) checklist and CRM skills by Ottawa Global Rating Scale. The groups were reassessed at 4 months. A third group, who received no CRM education, served as a control group. RESULTS: The mean BLS scores after CRM education were 18.9 and 24.9 with mean Ottawa Global Rating Scale (GRS) scores of 22.4 and 29.1 in the didactic teaching and noncontextual groups, respectively. The difference between intervention groups was significant for BLS (P = 0.02) and Ottawa GRS (P = 0.03) score. At 4-month follow-up, there was no statistically significant difference in BLS (P = 1.0) or Ottawa GRS score (P = 0.55) between intervention groups. In comparison with the control group, there was a marginally significant difference in Ottawa GRS score (P = 0.06) at 4-month follow-up. CONCLUSIONS: Noncontextual active learning of CRM using low-fidelity simulation results in improved CRM performance in comparison with didactic teaching. The benefits of CRM education do not seem to be sustained after one education session, suggesting the need for continued education and practice of skills to improve retention.
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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 | 0.034 | 0.134 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".