Linking Nurses' Clinical Leadership to Patient Care Quality: The Role of Transformational Leadership and Workplace Empowerment
Bibliographic record
Abstract
Background While improving patient safety requires strong nursing leadership, there has been little empirical research that has examined the mechanisms by which leadership influences patient safety outcomes. Aim To test a model examining relationships among transformational leadership, structural empowerment, staff nurse clinical leadership, and nurse-assessed adverse patient outcomes. Methods A cross-sectional survey was conducted with a randomly selected sample of 378 registered nurses working in direct patient care in acute care hospitals across Ontario, Canada. Structural equation modeling was used to test the hypothesized model. Results The model had an acceptable fit, and all paths were significant. Transformational leadership was significantly associated with decreased adverse patient outcomes through structural empowerment and staff nurse clinical leadership. Discussion This study highlights the importance of transformational leadership in creating empowering practice environments that foster high-quality care. The findings indicate that a more complete understanding of what drives desired patient outcomes warrants the need to focus on how to empower nurses and foster clinical leadership practices at the point of care. Conclusion In planning safety strategies, managers must demonstrate transformational leadership behaviors in order to modify the work environment to create better defenses for averting adverse events.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".