Nursing Leadership Models in Promoting and Improving Patient’s Safety Culture in Healthcare Facilities; A Systematic Review
Bibliographic record
Abstract
Background: Nurse leader has an important role in encouraging patient’s safety culture among nurses in the healthcare system. This literature review aims to identify the nursing leadership model and to promote and improve patient safety culture to improve patient outcomes in health care facilities including hospitals, primary health care, and nursing home settings. Methods: Searching appropriate journals through some journal databases were applied including DOAJ, GARUDA, Google Scholar, MDPI, Proquest, Pubmed, Sage Journals, ScienceDirect, and Wiley Online Library, which were published from 2015 to 2020. Results: Fourteen articles meet the criteria and are included in this review. The majority of these articles were retrieved from western countries, the US, Canada, and Finland. This review identifies three nursing leadership models that seem useful to promote and improve patient safety culture in health care facilities which are transformational, authentic, and ethical leadership models. Conclusion: The patient safety influences health care outcomes. The evidence shows the leadership has positive relation to patient satisfaction and patient safety outcomes improvement. The transformational, authentic, and ethical leadership models seem to be more useful in promoting, maintaining, and improving patient safety culture in health care facilities.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".