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Record W2467006977 · doi:10.1093/ageing/afw125

Social engagement and depressive symptoms: do baseline depression status and type of social activities make a difference?

2016· article· en· W2467006977 on OpenAlexaff
Joohong Min, Jennifer Ailshire, Eileen M. Crimmins

Bibliographic record

VenueAge and Ageing · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of Alberta
FundersNational Institute on AgingNational Institutes of Health
KeywordsDepression (economics)MedicineSocial engagementBaseline (sea)Depressive symptomsGerontologySignificant differencePsychiatryClinical psychologyInternal medicineCognitionSocial scienceSociology

Abstract

fetched live from OpenAlex

OBJECTIVES: our purpose is to examine whether the association between social engagement and depressive symptoms differs by initial level of depressive symptoms and by the types of social engagement in which older adults engage. DESIGN: persons aged 60 years and older in 2006 (n = 4,098) were drawn from Wave 1 of the Korean Longitudinal Study of Ageing and followed through Wave 3 (2010). Growth curve analyses were conducted to identify the association between engagement in multiple types of social activities and 4-year change in depressive symptoms. Depression trajectories are examined separately by baseline depression status. RESULTS: attending religious services was related to an increase in depressive symptoms and participating in social gatherings with friends and neighbours was related to a decrease in depressive symptoms, but only among persons with CES-D 10 scale score below 10 at baseline. CONCLUSIONS: our findings suggest that the positive effects of participating in social gatherings with friends and family are manifest among older adults who have good mental health to begin with. Our findings also suggest that the association between social engagement and mental health varies by type of engagement and initial depression level.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.720
Threshold uncertainty score0.598

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.036
GPT teacher head0.338
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 teacher head, 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

Citations92
Published2016
Admission routes1
Has abstractyes

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