Factors influencing job satisfaction of front line nurse managers: a systematic review
Bibliographic record
Abstract
AIM: The purpose of this study was to systematically review the research literature that examined the determinants of front line nurse managers' job satisfaction. BACKGROUND: Front line managers are the vital link between senior management and clinical nurses. They influence organizational culture and outcomes for patients and staff so their job satisfaction and ultimately retention is of importance. EVALUATIONS: A review of research articles that examined the determinants of front line nurse managers' job satisfaction was conducted. These managers supervise staff nurses and have direct responsibility for the management of a nursing unit or team in any type of healthcare facility. Fourteen studies were included in the final analysis. KEY ISSUES: Evidence of significant positive relationships were found between span of control, organizational support, empowerment and the job satisfaction of front line nurse managers. CONCLUSION: The review suggests that job satisfaction of front line managers may be improved by addressing span of control and workload, increasing organizational support from supervisors and empowering managers to participate in decision-making. IMPLICATIONS FOR NURSING MANAGEMENT: Healthcare organizations may enhance the recruitment, retention and sustainability of future nursing leadership by addressing the factors that influence job satisfaction of front line managers.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| 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".