Turnover of regulated nurses in long-term care facilities
Bibliographic record
Abstract
AIMS: To describe the relationship between nursing staff turnover in long-term care (LTC) homes and organisational factors consisting of leadership practices and behaviours, supervisory support, burnout, job satisfaction and work environment satisfaction. BACKGROUND: The turnover of regulated nursing staff [Registered Nurses (RNs) and Registered Practical Nurses (RPNs)] in LTC facilities is a pervasive problem, but there is a scarcity of research examining this issue in Canada. METHODS: The study was conceptualized using a Stress Process model. Distinct surveys were distributed to administrators to measure organisational factors and to regulated nurses to measure personal and job-related sources of stress and workplace support. In total, 324 surveys were used in the linear regression analysis to examine factors associated with high turnover rates. RESULTS: Higher leadership practice scores were associated with lower nursing turnover; a one score increase in leadership correlated with a 49% decrease in nursing turnover. A significant inverse relationship between leadership turnover and nurse turnover was found: the higher the administrator turnover the lower the nurse turnover rate. CONCLUSION: Leadership practices and administrator turnover are significant in influencing regulated nurse turnover in LTC. IMPLICATIONS FOR NURSING MANAGEMENT: Long-term care facilities may want to focus on building good leadership and communication as an upstream method to minimize nurse 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".