Nursing churn and turnover in Australian hospitals: nurses perceptions and suggestions for supportive strategies
Bibliographic record
Abstract
BACKGROUND: This study aimed to reveal nurses' experiences and perceptions of turnover in Australian hospitals and identify strategies to improve retention, performance and job satisfaction. Nursing turnover is a serious issue that can compromise patient safety, increase health care costs and impact on staff morale. A qualitative design was used to analyze responses from 362 nurses collected from a national survey of nurses from medical and surgical nursing units across 3 Australian States/Territories. METHOD: A qualitative design was used to analyze responses from 362 nurses collected from a national survey of nurses from medical and surgical nursing units across 3 Australian States/Territories. RESULTS: Key factors affecting nursing turnover were limited career opportunities; poor support; a lack of recognition; and negative staff attitudes. The nursing working environment is characterised by inappropriate skill-mix and inadequate patient-staff ratios; a lack of overseas qualified nurses with appropriate skills; low involvement in decision-making processes; and increased patient demands. These issues impacted upon heavy workloads and stress levels with nurses feeling undervalued and disempowered. Nurses described supportive strategies: improving performance appraisals, responsive preceptorship and flexible employment options. CONCLUSION: Nursing turnover is influenced by the experiences of nurses. Positive steps can be made towards improving workplace conditions and ensuring nurse retention. Improving performance management and work design are strategies that nurse managers could harness to reduce turnover.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".