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Record W4200094937 · doi:10.1093/geroni/igab046.1466

At-Homeness Influences the Cognition of Multimorbid Older Adults: Longitudinal Path Analysis Through Loneliness

2021· article· en· W4200094937 on OpenAlexaffabout
Daniel R Y Gan, John R. Best, Andrew Wister

Bibliographic record

VenueInnovation in Aging · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsLonelinessPsychologyDementiaDepression (economics)GerontologyCognitionStructural equation modelingPath analysis (statistics)Longitudinal studyLife satisfactionSocial supportCognitive declineClinical psychologyMedicinePsychiatrySocial psychologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Approximately two-thirds of older adults’ experience multimorbidity in North America. Challenges of symptoms management and reduced mobility often coincide with late-life depression which is associated with a 2 to 5-fold increased dementia risk. Loneliness and depression are connected in the prodromal phases. We examine the effects of physical environment (e.g., housing and neighborhood factors) and social environment (e.g., social support) on loneliness, depression, and cognition using path analysis, controlling for baseline. Data(n=15,087) was drawn from the Canadian Longitudinal Study on Aging. Measures of housing, neighborhood and life satisfaction were used to construct an index of “at-homeness” based on theory. We found good model fit (TLI=.989; CFI=.999; RMSEA=.026; SRMR=.006). At-homeness(B=-.20, p<.001) rivaled the effect of social environment(B=-.19, p<.001) on loneliness. Together, physical environment and loneliness had as much effect on cognition as depression. If causality is supported, modifying older adults’ satisfaction with their home environment may reduce loneliness and cognitive decline.

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)0.003
metaresearch head score (Gemma)0.007
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 score0.019
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.050
GPT teacher head0.380
Teacher spread0.330 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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