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Record W3217175235 · doi:10.3928/00989134-20211109-09

Differences in Care Provided in Urban and Rural Nursing Homes in the United States: Literature Review

2021· review· en· W3217175235 on OpenAlexaboutno aff
Denise D. Quigley, Leah V. Estrada, Gregory L. Alexander, Andrew W. Dick, Patricia W. Stone

Bibliographic record

VenueJournal of Gerontological Nursing · 2021
Typereview
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsnot available
FundersNational Institute of Nursing Research
KeywordsNursingScale (ratio)Quality (philosophy)Health careRural areaMedicineMEDLINEPsychologyGeographyPolitical science

Abstract

fetched live from OpenAlex

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.]

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.867
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.099
GPT teacher head0.452
Teacher spread0.353 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations12
Published2021
Admission routes1
Has abstractyes

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