Differences in Care Provided in Urban and Rural Nursing Homes in the United States: Literature Review
Bibliographic record
Abstract
Despite evidence acknowledging disadvantages in care provided to older adults in rural nursing homes (NHs) in the United States, since 2010, no literature review has focused on differences in care provided in urban versus rural NHs. In the current study, we examined these differences by searching U.S. English-language peer-reviewed articles published after 2010 on differences in care quality in urban and rural NHs. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and used the Newcastle-Ottawa Scale for quality appraisal. We conducted full-text abstraction of 56 (of 286) articles, identifying 10 relevant studies. Metric specification of urban/rural location varied, and care quality measures were wide-ranging, making it difficult to interpret evidence. Limited evidence supported that rural NHs, compared to urban NHs, provided sparse mental health support and limited access to hospice care after controlling for facility and resident characteristics. Our review highlights the need for more research examining differences in quality of care between urban and rural NHs and raises several issues in current research examining urban/rural NH differences where future work is needed. [Journal of Gerontological Nursing, 47(12), 48–56.]
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".