Leadership practices and staff nurses’ intent to stay: a systematic review
Bibliographic record
Abstract
AIM: The aim of the present study was to describe the findings of a systematic review of the literature that examined the relationship between managers' leadership practices and staff nurses' intent to stay in their current position. BACKGROUND: The nursing shortage demands that managers focus on the retention of staff nurses. Understanding the relationship between leadership practices and nurses' intent to stay is fundamental to retaining nurses in the workforce. METHODS: Published English language articles on leadership practices and staff nurses' intent to stay were retrieved from computerized databases and a manual search. Data extraction and quality assessments were completed for the final 23 research articles. RESULTS: Relational leadership practices influence staff nurses' intentions to remain in their current position. CONCLUSION: This study supports a positive relationship between transformational leadership, supportive work environments and staff nurses' intentions to remain in their current positions. Incorporating relational leadership theory into management practices will influence nurse retention. Advancing current conceptual models will increase knowledge of intent to stay. Clarifying the distinction between the concepts intent to stay and intent to leave is needed to establish a clear theoretical foundation for further intent to stay research. IMPLICATIONS FOR NURSE MANAGERS: Nurse managers and leaders who practice relational leadership and ensure quality workplace environments are more likely to retain their staff. The findings of the present study support the claim that leadership practices influence staff nurse retention and builds on intent to stay knowledge.
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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 | 0.014 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".