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Record W2730646385 · doi:10.1093/geroni/igx004.1607

ADULT DAY CENTRES AND THEIR OUTCOMES ON CLIENTS, CAREGIVERS, AND THE HEALTH SYSTEM: A SCOPING REVIEW

2017· review· en· W2730646385 on OpenAlexaff
Moriah Ellen, Peter DeMaio, A. Lang, Moira Wilson

Bibliographic record

VenueInnovation in Aging · 2017
Typereview
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsMcGill UniversityMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsPsychological interventionGerontologyHealth careMedicinePopulationPsychologyNursingEnvironmental health

Abstract

fetched live from OpenAlex

Purpose of the study: Adult day centers (ADCs) offer a heterogeneous group of services that provide for the daily living, care, nutritional, and social needs of older adults. We sought to conceptually map and identify key gaps and findings from literature focused on ADCs, including the types of programs that exist and their associated outcomes on improving health and strengthening health systems. Design and Methods: We conducted a scoping review by searching five databases for studies evaluating the outcomes of ADCs specifically for community-dwelling older adults. Included studies were conceptually mapped according to the methods used, type of outcome(s) assessed, study population, disease focus, service focus, and health system considerations. The mapping was used to derive descriptive analyses to profile the available literature in the area. Results: ADC use has positive health-related, social, psychological and behavioral outcomes for care recipients and caregivers. There is a substantial amount of literature available on some ADC use outcomes, such as health-related, satisfaction-related and psychological and behavioral outcomes, while less research exists on issues of accessibility and cost-effectiveness. Implications: As the population ages, policymakers must carefully consider how ADCs can best serve each user and their caregivers with their unique circumstances. ADCs have the potential to help shape health system interventions, especially those targeting caregivers and people requiring long-term care support. Due to the variation among types of ADC programs, future research on ADCs should consider different characteristics of ADC programs to better contextualize their results.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.832
Threshold uncertainty score0.751

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.094
GPT teacher head0.445
Teacher spread0.351 · 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.

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

Citations0
Published2017
Admission routes1
Has abstractyes

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