Associations between rationing of nursing care and inpatient mortality in Swiss hospitals
Bibliographic record
Abstract
OBJECTIVE: To explore the relationship between inpatient mortality and implicit rationing of nursing care, the quality of nurse work environments and the patient-to-nurse staffing ratio in Swiss acute care hospitals. DESIGN: Cross-sectional correlational design. SETTING: Eight Swiss acute care hospitals examined in a survey-based study and 71 comparison institutions. PARTICIPANTS: A total of 165 862 discharge abstracts from patients treated in the 8 RICH Nursing Study (the Rationing of Nursing Care in Switzerland Study) hospitals and 760 608 discharge abstracts from patients treated in 71 Swiss acute care hospitals offering similar services and maintaining comparable patient volumes to the RICH Nursing hospitals. MAIN OUTCOME MEASURES: The dependent variable was inpatient mortality. Logistic regression models were used to estimate the effects of the independent hospital-level measures. RESULTS: Patients treated in the hospital with the highest rationing level were 51% more likely to die than those in peer institutions (adjusted OR: 1.51, 95% CI: 1.34-1.70). Patients treated in the study hospitals with higher nurse work environment quality ratings had a significantly lower likelihood of death (adjusted OR: 0.80, 95% CI: 0.67-0.97) and those treated in the hospital with the highest measured patient-to-nurse ratio (10:1) had a 37% higher risk of death (adjusted OR: 1.37, 95% CI: 1.24-1.52) than those in comparison institutions. CONCLUSIONS: Measures of rationing may reflect care conditions that place hospital patients at risk of negative outcomes and thus deserve attention in future hospital outcomes research studies.
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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 | 0.001 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".