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Record W4323036114 · doi:10.1177/07334648231158490

Access and Use of Services by Caregivers of Older Adults: A Scoping Review of Cultural and Linguistic Diversity

2023· review· en· W4323036114 on OpenAlexaff
Danielle Knipping, Anna Garnett, Bingfang Bianca Jiang

Bibliographic record

VenueJournal of Applied Gerontology · 2023
Typereview
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsWestern University
Fundersnot available
KeywordsDiversity (politics)Language barrierInclusion (mineral)GerontologyCultural diversityMEDLINEHealth careMedicinePopulationPsychologySociology

Abstract

fetched live from OpenAlex

Westernized countries are home to an increasingly culturally and linguistically diverse (CLD) older adult population. Informal caregivers of CLD older adults face unique challenges accessing and using home- and community-based services (HCBS). This scoping review sought to identify facilitators and barriers to access and use of HCBS for informal caregivers of CLD older adults. Arksey and O'Malley's framework guided a systematic search of five electronic databases. The search strategy retrieved 5979 unique articles. Forty-two studies met the inclusion criteria and informed this review. Facilitators and barriers were identified at three stages of using services: knowledge, access, and use of services. Findings concerning access to HCBS were subdivided into willingness and ability to access HCBS. Results emphasize the need for changes in healthcare systems, organizations, and providers to provide culturally appropriate care and improve the accessibility and acceptability of HCBS for informal caregivers of CLD older adults.

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)0.034
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 score0.013
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.034
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0120.013
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.073
GPT teacher head0.389
Teacher spread0.316 · 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

Citations9
Published2023
Admission routes1
Has abstractyes

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