The influence of nursing leadership on nurse performance: a systematic literature review
Bibliographic record
Abstract
AIM: The aim was to explore leadership factors that influence nurse performance and particularly, the role that nursing leadership behaviors play in nurses' perceptions of performance motivation. BACKGROUND: Nurse performance is vital to quality patient care outcomes and nursing leadership behaviors have been linked to nurse performance. EVALUATIONS: A review of research articles that examined the factors that nurses perceived as influencing their motivation and performance was conducted. Eight studies were included in the final analysis. KEY ISSUES: Nurses' perceptions of factors that affect their motivation and ability to perform were grouped into five categories using content analysis: autonomy, work relationships, resource accessibility, nurse factors, and leadership practices. Nursing leadership behaviors were found to influence both nurses' motivations directly and indirectly via other factors. CONCLUSION: The review suggests that nurse performance may be improved by addressing nurse autonomy, relationships among nurses, their colleagues and leaders, and resource accessibility. IMPLICATIONS FOR NURSING MANAGEMENT: Nursing managers and leaders may enhance their nurses' performance by understanding and addressing the factors that affect their ability and motivation to perform.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".