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Record W2762245006 · doi:10.1177/0164027517734587

From Social Integration to Social Isolation: The Relationship Between Social Network Types and Perceived Availability of Social Support in a National Sample of Older Canadians

2017· article· en· W2762245006 on OpenAlexaffabout
Oksana Harasemiw, Nancy E. Newall, Shahin Shooshtari, Corey S. Mackenzie, Verena Menec

Bibliographic record

VenueResearch on Aging · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsBrandon UniversityUniversity of Manitoba
Fundersnot available
KeywordsSocial supportSocial network (sociolinguistics)Social isolationPsychologySocial engagementSample (material)Interpersonal tiesSocial competenceGerontologySocial psychologyDevelopmental psychologySocial changeSociologySocial mediaMedicineComputer sciencePolitical scienceWorld Wide Web

Abstract

fetched live from OpenAlex

It is well-documented that social isolation is detrimental to health and well-being. What is less clear is what types of social networks allow older adults to get the social support they need to promote health and well-being. In this study, we identified social network types in a national sample of older Canadians and explored whether they are associated with perceived availability of different types of social support (affectionate, emotional, or tangible, and positive social interactions). Data were drawn from the baseline questionnaire of the Canadian Longitudinal Study on Aging for participants aged 65-85 (unweighted n = 8,782). Cluster analyses revealed six social network groups. Social support generally declined as social networks became more restricted; however, different patterns of social support availability emerged for different social network groups. These findings suggest that certain types of social networks place older adults at risk of not having met specific social support needs.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
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 head0.250
GPT teacher head0.498
Teacher spread0.248 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations93
Published2017
Admission routes2
Has abstractyes

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