Adaptation of crew resource management training in high-risk industries
Bibliographic record
Abstract
Crew resource management (CRM) is a training concept for optimising the use of all available resources (e.g.human, technology, organisation) within high-risk operations.It has its origins in aviation, but the principles and methods of CRM are already used in many other industries, like medicine or fire service.In order to raise the reliability of teams working in these industries, it is important to train them in non-technical skills (e.g.cooperation, managerial and leadership skills, decision making or situation awareness).Despite many years of research on CRM training, the amount of detailed information about these training programmes is often limited.Thus, the picture about CRM is not as clear as it should be.This makes it difficult to assess the effectiveness of such training programmes to prevent human error and industrial accidents.During a research project on the adaptation of CRM training, scientists of Rhine-Waal University in Kamp-Lintfort (Germany) conducted an empirical study.They interviewed 10 CRM experts from civil and military aviation, aircraft engineering, fire service, seafaring and medicine, about their experiences with CRM training to gain an overview about the current state of CRM in different industries.They identified cross-industry and industry-specific lessons learned as well as success factors of these training programmes.The results can be used to improve current CRM programmes, which in turn may increase safety standards in high-risk work domains.
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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 | 0.004 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".