Nurse managers’ role in older nurses’ intention to stay
Bibliographic record
Abstract
PURPOSE: The purpose of this paper is to propose and test a model of the underlying mechanisms linking perceived availability of human resource (HR) practices relevant to older nurses and older nurses' intentions to stay with their hospitals. DESIGN/METHODOLOGY/APPROACH: Quantitative data were collected from randomly selected older registered nurses (N=660) engaged in direct patient care in hospitals in Canada. Structural equation modelling was used to test the hypothesized model. FINDINGS: The relationship between perceptions of HR practices (performance evaluation, recognition/respect) and intentions to stay was mediated by the perceived fairness with which nurse managers managed these HR practices and nurse manager satisfaction. When nurse managers were perceived to administer the HR practices fairly (high perceived procedural justice), older nurses were more satisfied with their nurse manager and, in turn, more likely to intend to stay. RESEARCH LIMITATIONS/IMPLICATIONS: The cross-sectional research design does not allow determination of causality. PRACTICAL IMPLICATIONS: It is important that nurse managers receive training to increase their awareness of the needs of older nurses and that nurse managers be educated on how to manage HR practices relevant to older nurses in a fair manner. Equally important is that hospital administrators and HR managers recognize the importance of providing such HR practices and supporting nurse managers in managing these practices. ORIGINALITY/VALUE: The findings increase the understanding of how HR practices tailored to older nurses are related to the intentions of these nurses to remain with their hospital, and especially the crucial role that first-line nurse managers play in this process.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".