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Record W3215936146 · doi:10.12927/hcpol.2021.26654

A Scoping Review of the Implementation of Local Health and Social Services for Older Adults

2021· review· en· W3215936146 on OpenAlexaffvenueabout
Alexandra Éthier, Annie Carrier

Bibliographic record

VenueHealthcare policy · 2021
Typereview
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
Fundersnot available
KeywordsIdentification (biology)Health servicesSocial WelfareService (business)BusinessGerontologyKnowledge managementPublic relationsProcess managementPsychologyMedicineComputer sciencePolitical scienceEnvironmental healthMarketing

Abstract

fetched live from OpenAlex

BACKGROUND: Implementing elder-dedicated local health and social services (LHSS) is primary for older Canadian adults to age in place. However, there is currently no synthesis of the factors (barriers and facilitators) involved in LHSS implementation. OBJECTIVE: This study aimed to synthesize current knowledge about the institutional factors involved in elder-dedicated LHSS implementation by describing them and their influence. METHODS: A scoping review was conducted using eight databases and the grey literature. Data were analyzed thematically. RESULTS: A total of 23 documents led to the identification of 15 inter-influencing factors (12 barriers and 11 facilitators). Indeed, 20 connections were noted among factors, mostly among barriers. DISCUSSION AND IMPLICATION: Although some barriers and facilitators also affect the implementation of services dedicated to the general population in Canada, the interplay between agism and power issues needs to be taken into consideration for a successful elder-dedicated LHSS implementation.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.475
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.108
GPT teacher head0.572
Teacher spread0.464 · 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 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

Citations5
Published2021
Admission routes3
Has abstractyes

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