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Record W4309322782 · doi:10.1177/08982643221129686

The Effects of Loneliness on Depressive Symptoms Among Older Adults During COVID-19: Longitudinal Analyses of the Canadian Longitudinal Study on Aging

2022· article· en· W4309322782 on OpenAlexafffundabout
Andrew Wister, Lun Li, Mélanie Levasseur, Laura Kadowaki, John Pickering

Bibliographic record

VenueJournal of Aging and Health · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsMacEwan UniversityCentre Hospitalier Universitaire de SherbrookeUniversité de SherbrookeSimon Fraser University
FundersCanadian Institutes of Health Research
KeywordsLonelinessLongitudinal studyDepression (economics)Depressive symptomsCoronavirus disease 2019 (COVID-19)UCLA Loneliness ScalePandemicPsychologyMental healthPublic healthGerontologyHealth and Retirement StudyClinical psychologyCenter for Epidemiologic Studies Depression ScaleMedicinePsychiatryCognition

Abstract

fetched live from OpenAlex

Objectives This paper examines the longitudinal effects of changes in the association between loneliness and depressive symptoms during the pandemic among older adults (65+). Methods Baseline (2011–2015) and Follow-up 1 (2015–2018) from the Canadian Longitudinal Study on Aging (CLSA), and the Baseline and Exit waves of the CLSA COVID-19 study (April–December, 2020) ( n = 12,469) were used. Loneliness was measured using the 3-item UCLA Loneliness Scale and depression using the CES_D- 9. Results Loneliness is associated with depressive symptoms pre-pandemic; and changes in level of loneliness between FUP1 and the COVID Exit survey, adjusting for covariates. No interaction between loneliness and caregiving, and with multimorbidity, on depressive symptoms were observed, and several covariates exhibited associations with depressive symptoms. Discussion Strong support is found for an association between loneliness on depressive symptoms among older adults during the pandemic. Public health approaches addressing loneliness could reduce the burden of depression on older populations.

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)0.003
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 score0.028
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.063
GPT teacher head0.419
Teacher spread0.356 · 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

Citations19
Published2022
Admission routes3
Has abstractyes

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