Human resource management practices and nursing turnover
Bibliographic record
Abstract
Background: The objective of this study is to explore the nature of the association of discrete systems of human resource management practices and the rate of voluntary turnover of registered nurses (RNs) in a large sample of Canadian acute care hospitals and nursing homes. Turnover of nursing staff is a significant issue affecting healthcare cost, quality and access. The rate of annual turnover for registered nurses has been estimated to be about 20 percent in Canada, with a total cost of almost US$27,000 for each RN. Healthcare organizations that employ large numbers of registered nurses are keen to learn more about the role that organizational policies and practices play in reducing voluntary turnover, including the role of management and its deployment of human resources. The relationship of human resource management (HRM) practices and employee turnover has generally been under-investigated. Three HRM systems, composed of bundles of discrete workplace and employment practices, were assembled in order to assess the relationship of HRM practice systems with the rate of voluntary RN turnover, at the establishment level. The three HRM practice systems constructed are: a technical HRM system that focuses on policies and practices that regulate the employee-employer relationship; a quality-of-worklife HRM system that includes a variety of employee- and family-friendly employment practices; a high-involvement HRM system that involve workplace arrangements that increase commitment, engagement and participation of employees. Method and analysis: A questionnaire was sent to the chief nursing officers of 2,208 hospitals and long-term care facilities in every province and territory of Canada, yielding valid responses from 705 establishments. Each of the three HRM practice systems is estimated at each facility and comprises eight (8) discrete employment and workplace practices. Using estimates of voluntary RN turnover as the dependent variable, the analysis featured a step-wise ordinary least squares (OLS) regression model for each practice system. Results: After controlinf for facility characteristics and local labour market conditions, the quality-of-worklife HRM employment system ( p <.05) and high-involvement HRM works system ( p <.05) are found to be associated with lower voluntary turnover, yet their overall relationship with facility turnover appears to be quite modest. Our technical HRM system is not found to be associated with facility RN turnover. Conclusion: HRM systems comprising employee-friendly employment practices and nursing high-involvement work practices may play a significant (yet modest) role in reducing RN turnover.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".