Bibliographic record
Abstract
PURPOSE OF REVIEW: Error is ubiquitous in medicine, particularly during critical events and resuscitation. A significant proportion of adverse events can be attributed to inadequate team-based skills such as communication, leadership, situation awareness and resource utilization. Aviation-based crisis resource management (CRM) training using high-fidelity simulation has been proposed as a strategy to improve team behaviours. This review will address key considerations in CRM training and outline recommendations for the future of human factors education in healthcare. RECENT FINDINGS: A critical examination of the current literature yields several important considerations to guide the development and implementation of effective simulation-based CRM training. These include defining a priori domain-specific objectives, creating an immersive environment that encourages deliberate practice and transfer-appropriate processing, and the importance of effective team debriefing. Building on research from high-risk industry, we suggest that traditional CRM training may be augmented with new training techniques that promote the development of shared mental models for team and task processes, address the effect of acute stress on team performance, and integrate strategies to improve clinical reasoning and the detection of cognitive errors. SUMMARY: The evolution of CRM training involves a 'Triple Threat' approach that integrates mental model theory for team and task processes, training for stressful situations and metacognition and error theory towards a more comprehensive training paradigm, with roots in high-risk industry and cognitive psychology. Further research is required to evaluate the impact of this approach on patient-oriented outcomes.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".