Nursing Staff Time and Care Quality in Long-Term Care Facilities: A Systematic Review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: In long-term care (LTC) facilities, nursing staff are important contributors to resident care and well-being. Despite this, the relationships between nursing staff coverage, care hours, and quality of resident care in LTC facilities are not well understood and have implications for policy-makers. This systematic review summarizes current evidence on the relationship between nursing staff coverage, care hours, and quality of resident care in LTC facilities. RESEARCH DESIGN AND METHODS: A structured literature search was conducted using four bibliographic databases and gray literature sources. Abstracts were screened by two independent reviewers using Covidence software. Data from the included studies were summarized using a pretested extraction form. The studies were critically appraised, and their results were synthesized narratively. RESULTS: The systematic searched yielded 15,842 citations, of which 54 studies (all observational) were included for synthesis. Most studies (n = 53, 98%) investigated the effect of nursing staff time on resident care. Eleven studies addressed minimum care hours and quality of care. One study examined the association between different nursing staff coverage models and resident outcomes. Overall, the quality of the included studies was poor. DISCUSSION AND IMPLICATIONS: Because the evidence was inconsistent and of low quality, there is uncertainty about the direction and magnitude of the association between nursing staff time and type of coverage on quality of care. More rigorously designed studies are needed to test the effects of different cutoffs of care hours and different nursing coverage models on the quality of resident care in LTC facilities.
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.
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 | 0.022 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| 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".