53 Associations of four nurse staffing practices with hospital mortality: key lessons for hospital managers and leaders
Bibliographic record
Abstract
<h3>Background</h3> Cross-sectional studies of hospital-level administrative data have suggested that four nurse staffing practices – using adequate staffing levels, higher proportions of Registered Nurses (RNs) (skill mix), and more educated and experienced RNs – are each associated with reduced hospital mortality. To increase the validity of this evidence, patient-level longitudinal studies assessing the simultaneous associations of these staffing practices with mortality are required. <h3>Methods</h3> A dynamic cohort of 146,349 adult medical, surgical, and intensive care patients admitted to a Canadian university health center was followed for seven years (2010–2017). We used a multivariable Cox proportional hazards model to estimate the associations between patients’ time-varying cumulative exposure to measures of RN understaffing, skill mix, education and experience, each relative to nursing unit and shift means, and the hazard of in-hospital mortality, while adjusting for patient and nursing unit characteristics, and modeling the current nursing unit of hospitalization as a random effect. <h3>Results</h3> Overall, 4,854 in-hospital deaths occurred during 3,478,603 patient-shifts of follow-up (13.95 deaths/10,000 patient-shifts). In multivariable analyses, every 5% increase in the cumulative proportion of understaffed shifts was associated with a 1.0% increase in mortality (HR: 1.010; 95%CI: 1.002–1.017; p = 0.009). Moreover, every 5% increase in the cumulative proportion of worked hours by baccalaureate-prepared RNs was associated with a 2.0% reduction of mortality (HR: 0.980; 95%CI: 0.965–0.995, p = 0.008). RN experience and skill mix were not significantly associated with mortality. <h3>Conclusions</h3> Reducing the frequency of understaffed shifts and increasing the proportion of baccalaureate-prepared RNs are associated with reduced hospital mortality.
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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 | 0.001 |
| 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".