Nurses’ intention to leave, nurse workload and in-hospital patient mortality in Italy: A descriptive and regression study.
Bibliographic record
Abstract
Higher nurse-to-patient ratios are associated with poor patient care and adverse nurse outcomes, including emotional exhaustion and intention to leave. We examined the effect of nurses' intention to leave and nurse-patient workload on in-hospital patient mortality in Italy. A multicentered descriptive and regression study using clinical data of patients aged 50 years or older with a hospital stay of at least two days admitted to surgical wards linked with nurse variables including workload and education levels, work environment, job satisfaction, intention to leave, nurses' perception of quality and safety of care, and emotional exhaustion. The final dataset included 15 hospitals, 1046 nurses, and 37,494 patients. A 10 % increase in intention to leave and an increase of one unit in nurse-patient workload increased likelihood of inpatient hospital mortality by 14 % (odds ratio 1.14; 1.02-1.27 95 % CI) and 3.4 % (odds ratio 1.03; 1.00-1.06 95 % CI), respectively. No other studies have reported a significant association between intention to leave and patient mortality. To improve patient outcomes, the healthcare system in Italy needs to implement policies on safe human resources policy stewardship, leadership, and governance to ensure nurse wellbeing, higher levels of safety, and quality nursing care.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".