Behavioral integrity for safety, priority of safety, psychological safety, and patient safety: A team-level study.
Bibliographic record
Abstract
This article clarifies how leader behavioral integrity for safety helps solve follower's double bind between adhering to safety protocols and speaking up about mistakes against protocols. Path modeling of survey data in 54 nursing teams showed that head nurse behavioral integrity for safety positively relates to both team priority of safety and psychological safety. In turn, team priority of safety and team psychological safety were, respectively, negatively and positively related with the number of treatment errors that were reported to head nurses. We further demonstrated an interaction effect between team priority of safety and psychological safety on reported errors such that the relationship between team priority of safety and the number of errors was stronger for higher levels of team psychological safety. Finally, we showed that both team priority of safety and team psychological safety mediated the relationship between leader behavioral integrity for safety and reported treatment errors. These results suggest that although adhering to safety protocols and admitting mistakes against those protocols show opposite relations to reported treatment errors, both are important to improving patient safety and both are fostered by leaders who walk their safety talk.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".