Bibliographic record
Abstract
OBJECTIVE: To quantify effects of aviation-based crew resource management training on patient safety-related behaviors and perceived personal empowerment. DESIGN: Prospective study of checklist use, error self-reporting, and a 10-point safety empowerment survey after participation in a crew resource management training intervention. SETTING: Seven hundred twenty-two-bed university hospital; 247-bed affiliated community hospital. PARTICIPANTS: There were 857 participants, the majority of whom were nurses (50%), followed by ancillary personnel (28%) and physicians (22%). MAIN OUTCOME MEASURES: Preoperative checklist use over time; number and type of entries on a Web-based incident reporting system; and measurement of degree of empowerment (1-5 scale) on a 10-point survey of safety attitudes and actions given prior to, immediately after, and a minimum of 2 months after training. RESULTS: Since 2003, 10 courses trained 857 participants in multiple disciplines. Preoperative checklist use rose (75% in 2003, 86% in 2004, 94% in 2005, 98% in 2006, and 100% in 2007). Self-initiated reports increased from 709 per quarter in 2002 to 1481 per quarter in 2008. The percentage of reports related to environment as opposed to actual events increased from 15.9% prior to training to 20.3% subsequently (P < .01). Perceived self-empowerment, creating a culture of safety, rose by an average of 0.5 point in all 10 realms immediately posttraining (mean [SD] rating, 3.0 [0.07] vs 3.5 [0.05]; P < .05). This was maintained after a minimum of 2 months. There was a trend toward a hierarchical effect with participants less comfortable confronting incompetence in a physician (mean [SD] rating, 3.1 [0.8]) than in nurses or technicians (mean [SD] rating, 3.4 [0.7] for both) (P>.05). CONCLUSIONS: Crew resource management programs can influence personal behaviors and empowerment. Effects may take years to be ingrained into the culture.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".