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Record W2753509725 · doi:10.1177/1539449217727119

Understanding Social Isolation Among Urban Aging Adults: Informing Occupation-Based Approaches

2017· article· en· W2753509725 on OpenAlexaff
Carri Hand, Jessica H. Retrum, G. E. Ware, P. G. Iwasaki, Gabe Moaalii, Deborah S. Main

Bibliographic record

VenueOTJR Occupational Therapy Journal of Research · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsWestern University
FundersNational Center for Advancing Translational SciencesUniversity of Colorado Denver
KeywordsSocial isolationIsolation (microbiology)Social engagementCitizen journalismSocial network (sociolinguistics)GerontologyInterpersonal tiesPsychologySociologySocial psychologyMedicinePolitical scienceSocial mediaSocial sciencePsychiatry

Abstract

fetched live from OpenAlex

Socially isolated aging adults are at risk of poor health and well-being. Occupational therapy can help address this issue; however, information is needed to guide such work. National surveys characterize social isolation in populations of aging adults but fail to provide meaningful information at a community level. The objective of this study is to describe multiple dimensions of social isolation and related factors among aging adults in diverse urban neighborhoods. Community-based participatory research involving a door-to-door survey of adults 50 years and older was used. Participants ( N = 161) reported social isolation in terms of small social networks (24%) and wanting more social engagement (43%). Participants aged 50 to 64 years reported the highest levels of isolation in most dimensions. Low income, poor health, lack of transportation, and infrequent information access appeared linked to social isolation. Occupational therapists can address social isolation in similar urban communities through policy and practice that facilitate social engagement and network building.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.540
GPT teacher head0.501
Teacher spread0.039 · 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.

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

Citations28
Published2017
Admission routes1
Has abstractyes

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