Nurse staffing levels and hospital mortality in critical care settings: literature review and meta‐analysis
Bibliographic record
Abstract
AIM: This paper reports a review of the literature on the association between critical care nurse staffing levels and patient mortality. BACKGROUND: Statistically significant inverse associations between levels of nurse staffing and hospital mortality have not been consistently found in the literature. Critical care settings are ideal to address this relationship due to high patient acuity and mortality, high intensity of the nursing care required, and availability of individual risk adjustment methods. METHODS: Major electronic databases were searched, including MEDLINE, EMBASE, and the Cumulative Index of Nursing and Allied Health Literature. The search terms included critical/intensive care, quality of health care, mortality/hospital mortality, personnel staffing and scheduling, and nursing staff (hospital). Only papers published in English were included. The original search was conducted in 2002 and updated in 2005. RESULTS: Nine studies were selected from 251 references screened. All nine were observational. Six were conducted in the United States of America, one in Austria, one in Brazil, and one in Scotland. The unadjusted risk ratio of nurse staffing (high vs. low) on hospital mortality were combined meta-analytically (five studies). The pooled estimate was 0.65 (95% confidence interval 0.47-0.91). However, after adjusting for various covariates within each study, the individually reported associations between high nurse staffing and low hospital mortality became non-significant in all but one study. CONCLUSION: The impact of nurse staffing levels on patients' hospital mortality in critical care settings was not evident in the reviewed studies. Methodological challenges that might have impeded correct assessment of the association include measurement problems in exposure status and confounding factors, often uncontrolled. The lack of association also indicates that hospital mortality may not be sensitive enough to detect the consequences of low nurse staffing levels in critical care settings.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.027 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".