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Record W4220741066 · doi:10.1017/s0714980822000046

Access to Long-Term Care for Minority Populations: A Systematic Review

2022· article· en· W4220741066 on OpenAlexafffund

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2022
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of OttawaBruyèreOttawa Hospital
FundersOntario Ministry of Health and Long-Term Care
KeywordsLanguage barrierQualitative researchSexual minorityConfoundingPerceptionMEDLINEQuality (philosophy)Qualitative property

Abstract

fetched live from OpenAlex

It has been shown that there is disparity in access to long-term care and other services for minority populations. This study assessed long-term care access among older individuals belonging to minority populations including visible, ethnocultural, linguistic, and sexual minorities. Barriers and facilitators influencing admission were identified and evaluated.A search for articles from 10 databases published between January 2000 and January 2021 was conducted. Included studies evaluated factors affecting minority populations' admission to long-term care, and non-residents' perceptions of future admission. This review was registered with PROSPERO: CRD42018038662. Sixty included quantitative and qualitative studies, ranging in quality from fair to excellent. Findings suggest minority status is associated with reduced admission to long-term care, controlling for confounding variables. Barriers identified include discordant language, fear of discrimination, lack of information, and family obligations. Findings suggest that minority populations experienced barriers accessing long-term care and had unmet cultural and language needs while receiving care in this setting.

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 imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.017
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.044
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.004
Bibliometrics0.0110.014
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.047
GPT teacher head0.355
Teacher spread0.308 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations7
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal on Aging / La Revue canadienne du vieillissement→Same topicGeriatric Care and Nursing Homes→French-language works237,207→