The link between leadership and safety outcomes in hospitals
Bibliographic record
Abstract
AIM: To test and refine a model examining relationships among leadership, interactional justice, quality of the nursing work environment, safety climate and patient and nurse safety outcomes. BACKGROUND: The quality of nursing work environments may pose serious threats to patient and nurse safety. Justice is an important element in work environments that support safety initiatives yet little research has been done that looks at how leader interactional justice influences safety outcomes. METHOD: A cross-sectional survey was conducted with 600 acute care registered nurses (RNs) to test and refine a model linking interactional justice, the quality of nurse leader-nurse relationships, work environment and safety climate with patient and nurse outcomes. RESULTS: In general the hypothesized model was supported. Resonant leadership and interactional justice influenced the quality of the leader-nurse relationship which in turn affected the quality of the work environment and safety climate. This ultimately was associated with decreased reported medication errors, intentions to leave and emotional exhaustion. CONCLUSIONS: Quality relationships based on fairness and empathy play a pivotal role in creating positive safety climates and work environments. IMPLICATIONS FOR NURSING MANAGEMENT: To advocate for safe work environments, managers must strive to develop high-quality relationships through just leadership practices.
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.
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 | 0.005 | 0.035 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".