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Record W4385663707 · doi:10.1017/s0714980823000302

Friendly Visiting Programs for Older People Experiencing Social Isolation: A Realist Review of what Works, for whom, and under what Conditions

2023· review· en· W4385663707 on OpenAlexaff
Rachel Weldrick, James R. Dunn, Gavin J. Andrews, Jenny Ploeg

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2023
Typereview
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsMcMaster UniversityToronto Metropolitan UniversitySimon Fraser University
Fundersnot available
KeywordsSocial isolationIsolation (microbiology)RecreationPsychological interventionPublic relationsPsychologyPolitical sciencePsychotherapistPsychiatry

Abstract

fetched live from OpenAlex

Many social interventions have been developed with the hopes of reducing and preventing social isolation among older people (e.g., recreation, arts-based programs and social prescription). Friendly visiting programs, also known as befriending schemes, have been a mainstay in this area for decades and are largely thought to be effective at reconnecting older people (≥ 60 years of age) experiencing isolation. Research and evaluations have yet to determine, however, how and why these programs may be most successful, and under what conditions. This article presents the findings of a realist synthesis aimed at identifying the critical mechanisms and contextual factors that lead to successful outcomes in friendly visiting programs. Seven studies are synthesized to inform a friendly visiting program theory accounting for key mechanisms (e.g., provision of informal support) and underlying contexts (e.g., training of volunteers) that can be used to inform future programs. Recommendations for future research are also presented.

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)0.004
metaresearch head score (Gemma)0.012
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.005
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.051
GPT teacher head0.353
Teacher spread0.302 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

Explore more

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