The Association Between Nursing Factors and Patient Mortality in the Veterans Health Administration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it