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Record W2899364536 · doi:10.3390/geriatrics3040075

A Scoping Review: Social Participation as a Cornerstone of Successful Aging in Place among Rural Older Adults

2018· article· en· W2899364536 on OpenAlexaff
Lisa Carver, Rob Beamish, Susan P. Phillips, Michelle Villeneuve

Bibliographic record

VenueGeriatrics · 2018
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsAging in placeGerontologyMedicineSocial capitalRural areaCornerstoneResidenceSocial supportSocial engagementHealthy agingPsychologySocial psychologySociologyGeography

Abstract

fetched live from OpenAlex

Despite obstacles, many rural-dwelling older adults report that positive aspects of rural residence, such as attachment to community, social participation, and familiarity, create a sense of belonging that far outweighs the negative. By being part of a community where they are known and they know people, rural elders continue to find meaning, the key to achieving successful aging in this last stage of life. This scoping review explored factors influencing social participation and, through it, successful aging among rural-dwelling older adults. We sought to answer the question: what factors enhance or detract from the ability of rural-dwelling older adults to engage in social participation in rural communities? The scoping review resulted in 19 articles that highlight the importance of supports to enable older people to spend time with others, including their pets, engage in volunteer and community activities, and help maintain their home and care for their pets. Overall, the lack of services, including local health care facilities, was less important than the attachment to place and social capital associated with aging in place.

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)0.052
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 score0.012
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.052
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0090.012
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0010.002
Research integrity0.0030.002
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 head0.028
GPT teacher head0.404
Teacher spread0.376 · 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

Citations117
Published2018
Admission routes1
Has abstractyes

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