Transformational Leadership and Nursing Retention: An Integrative Review
Bibliographic record
Abstract
Aim . To establish current evidence on the relationship between transformational nursing leadership and turnover intention. Background . The persistent nursing shortage in healthcare has led to heightened demands for addressing both current needs and the healthcare requirements of a growing population. Recognizing the pivotal role of nursing leadership in fostering retention, this review highlights the influence of positive leadership on nursing staff. Evaluation . An integrative review, guided by Whittemore and Knafl’s (2005) framework, was conducted using articles sourced from four online databases deducing to an inclusion of sixteen quantitative articles, one systematic review, and one integrative review published between 1992 and 2022. Key Issues . The study reveals conflicting evidence regarding the sole impact of transformational leadership on the nursing staff’s intention to remain. However, it highlights transformational leadership’s ability to enhance job satisfaction and organizational commitment contributes significantly to retention. Conclusion . Using transformational leadership can effectively bolster nursing staff retention along with promoting other favorable workplace outcomes. Implications for Nursing Management . This review underscores the importance of enhancing leadership skills within nursing management. This involves not only fostering transformational leadership but also cultivating positive work‐related outcomes to optimize nursing staff retention.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".