Nursing Home Staffing Standards and Staffing Levels in Six Countries
Bibliographic record
Abstract
PURPOSE: This study was designed to collect and compare nurse staffing standards and staffing levels in six counties: the United States, Canada, England, Germany, Norway, and Sweden. DESIGN: The study used descriptive information on staffing regulations and policies as well as actual staffing levels for registered nurses, licensed nurses, and nursing assistants across states, provinces, regions, and countries. METHODS: Data were collected from Internet searches of staffing regulations and policies along with statistical data on actual staffing from reports and documents. Staffing data were converted to hours per resident day to facilitate comparisons across countries. FINDINGS: We found wide variations in both nurse staffing standards and actual staffing levels within and across countries, although comparisons were difficult to make due to differences in measuring staffing, the vagueness of standards, and limited availability of actual staffing data. Both the standards and levels in most countries (except Norway and Sweden) were lower than the recommended levels by experts. CONCLUSIONS: Our findings demonstrate the need for further attention to nurse staffing standards and levels in order to assure the quality of nursing home care. CLINICAL RELEVANCE: A high quality of nursing home care requires adequate levels of nurse staffing, and nurse staffing standards have been shown to improve staffing levels.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".