Nurse-staffing level and quality of acute care services: Evidence from cross-national panel data analysis in OECD countries
Bibliographic record
Abstract
BACKGROUND: Most of previous studies aimed to estimate the effect of nurse staffing on quality of acute hospital care have used stochastic methods and their results are mixed. OBJECTIVE: To measure the magnitude of effect of nurse-staffing level on increasing quality of acute care services in long-run. DATA: The number of practicing nurses' density per 1000 population as the proxy of nurse-staffing level and three Health Care Quality Indicators (HCQI) included 30-day mortality per 100 patients based on acute myocardial infarction (MORTAMIO), hemorrhagic stroke (MORTHSTO) and ischemic stroke (MORTISTO) were collected as a part of ongoing project by OECD.org in panels of 26 OECD countries over 2005-2015 period. METHOD: Panel data analysis. RESULTS: There were committed relationships from nurse-staffing level to the enhancement of HCQI i.e. 1% increase in nurse-staffing level would reduce the rates of patient mortality based on MORTAMIO, MORTHSTO and MORTISTO by 0.65%, 0.60% and 0.80%, respectively. Furthermore, the role of nurse-staffing level in increasing overall HCQI were simulated at the highest level in Sweden (-3.53), Denmark (-3.31), Canada (-2.59), Netherlands (-2.33), Finland (-2.09), Switzerland (-1.72), Australia (-1.64) and United States (-1.53). CONCLUSION: A higher proportion of nurses-staffing level is associated with higher quality of acute care services in OECD countries. Also, the nursing characteristics of Sweden, Denmark, Canada, Netherlands, Finland, Switzerland, Australia and United States would be good patterns for other countries to maximize nursing outcomes in the care of patients with acute and life-threatening conditions by reducing the risk of complication, mortality and adverse clinical outcomes.
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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 | 0.003 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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".