Factors influencing nurse managers' intent to stay or leave: a quantitative analysis
Bibliographic record
Abstract
AIM: To identify and report on the relative importance of factors influencing nurse managers' intentions to stay in or leave their current position. BACKGROUND: Effective nurse managers play an important role in staff nurse retention and in the quality of patient care. The advancing age of nurse managers, multiple job opportunities within nursing and the generally negative perceptions of the manager role can contribute to difficulties in retaining nurse managers. METHODS: Ninety-five Canadian nurse managers participated in a web survey. Respondents rated the importance of factors related to their intent to leave or stay in their current position for another 2 years. Descriptive, t-test and mancova statistics were used to assess differences between managers intending to stay or leave. RESULTS: For managers intending to leave (n = 28), the most important factors were work overload, inability to ensure quality patient care, insufficient resources, and lack of empowerment and recognition. Managers intending to leave reported significantly lower job satisfaction, perceptions of their supervisor's resonant leadership and higher burnout levels. IMPLICATIONS FOR NURSING MANAGEMENT: Organisations wishing to retain existing nurse managers and to attract front-line staff into leadership positions must create and foster an environment that supports nurse 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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".