The Association Between Nursing Factors and Patient Mortality in the Veterans Health Administration
Bibliographic record
Abstract
CONTEXT: Nurse staffing is not the same across an entire hospital. Nursing care is delivered in geographically-based units, with wide variation in staffing levels. In particular, staffing in intensive care is much richer than in nonintensive care acute units. OBJECTIVE: To evaluate the association of in-hospital patient mortality with registered nurse staffing and skill mix comparing hospital and unit level analysis using data from the Veterans Health Administration (VHA). DESIGN, SETTINGS, AND PATIENTS: A retrospective observational study using administrative data from 129,579 patients from 453 nursing units (171 ICU and 282 non-ICU) in 123 VHA hospitals. METHODS: We used hierarchical multilevel regression models to adjust for patient, unit, and hospital characteristics, stratifying by whether or not patients had an ICU stay during admission. MAIN OUTCOME MEASURE: In-hospital mortality. RESULTS: : Of the 129,579 patients, mortality was 2.9% overall: 6.7% for patients with an ICU stay compared with 1.6% for those without. Whether the analysis was done at the hospital or unit level affected findings. RN staffing was not significantly associated with in-hospital mortality for patients with an ICU stay (OR, 1.02; 95% CI, 0.99-1.03). For non-ICU patients, increased RN staffing was significantly associated with decreased mortality risk (OR, 0.91; 95% CI, 0.86-0.96). RN education was not significantly associated with mortality. CONCLUSIONS: Our findings suggest that the association between RN staffing and skill mix and in-hospital patient mortality depends on whether the analysis is conducted at the hospital or unit level. Variable staffing on non-ICU units may significantly contribute to in-hospital mortality risk.
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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 | 0.003 |
| 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 | 0.001 |
| 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".