Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it