Implementation and Effectiveness of Crew Resource Management in the Medical Sector
Bibliographic record
Abstract
Crew Resource Management (CRM) is a simulation-based team training that strives to reduce human errors in emergencies and to increase patient safety by improving nontechnical skills.This qualitative study examines the current implementation and effectiveness of medical CRM training in German-speaking countries (Germany and Switzerland).Data was collected through interviews with 20 experts who conduct CRM training in various disciplines and application contexts.The material was analyzed first, using qualitative content analysis, and second, a frequency analysis was conducted.In order to ensure inter-rater reliability, Cohen's kappa was calculated.The results are consistent with research and showed that CRM in German-speaking countries is mainly based on the same principles, and training is conducted similarly.However, CRM is not widespread yet and requires consistent standards.Improvement in behavior in everyday professional life after training sessions have been observed, but no clear evidence of effectiveness on the outcome of the training has been provided to this point.Utilizing this study, German-speaking CRM applicants can compare their training implementation with that of the presented sample.This study is the first to assess the current implementation and effectiveness of CRM in German-speaking countries from the perspective of different disciplines and professions in the medical sector.
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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 | 0.012 | 0.025 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".