Impact and determinants of nurse turnover: a pan-Canadian study
Bibliographic record
Abstract
AIM: As part of a large study of nursing turnover in Canadian hospitals, the present study focuses on the impact and key determinants of nurse turnover and implications for management strategies in nursing units. BACKGROUND: Nursing turnover is an issue of ever-increasing priority as work-related stress and job dissatisfaction are influencing nurses' intention to leave their positions. METHODS: Data sources included the nurse survey, unit managers, medical records and human resources databases. A broad sample of hospitals was represented with nine different types of nursing units included. RESULTS: Nurses turnover is a major problem in Canadian hospitals with a mean turnover rate of 19.9%. Higher levels of role ambiguity and role conflict were associated with higher turnover rates. Increased role conflict and higher turnover rates were associated with deteriorated mental health. Higher turnover rates were associated with lower job satisfaction. Higher turnover rate and higher level of role ambiguity were associated with an increased likelihood of medical error. CONCLUSION: Managing turnover within nursing units is critical to high-quality patient care. A supportive practice setting in which role responsibilities are understood by all members of the caregiver team would promote nurse retention. IMPLICATIONS FOR NURSING MANAGEMENT: Stable nurse staffing and adequate managerial support are essential to promote job satisfaction and high-quality patient care.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".