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Record W2017401689 · doi:10.1177/0898264311422599

Aging and Late-Life Depression

2011· article· en· W2017401689 on OpenAlexafffund
Zheng Wu, Christoph M. Schimmele, Neena L. Chappell

Bibliographic record

VenueJournal of Aging and Health · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of Victoria
FundersHuman Resources and Skills Development Canada
KeywordsDepression (economics)Late life depressionGerontologyPsychologyPsychiatryMedicineCognitionEconomics

Abstract

fetched live from OpenAlex

OBJECTIVES: The objective of this study is to examine the relationship between age and depression among people aged 65 and older. METHOD: The study uses three waves of longitudinal data (1991, 1996, 2001) from a community and institutional sample of Canadians aged 65 and older. The study uses generalized linear mixed-model techniques to estimate the trajectories of depressive symptoms and major depression in late life. RESULTS: There is a linear increase in depressive symptoms after age 65, but this occurs in the context of medical comorbidity and is not an independent effect of aging. There is a significant u-shaped relationship between age and major depression, after adjusting for selected covariates. DISCUSSION: The relationship between age and late-life depression is complex, and it depends on how the dependent variable is measured. Late-life depression develops through a different set of risk factors than it does in earlier stages of the life course. The "fourth age" appears to be a period of psychiatric morbidity.

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)0.002
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 score0.040
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.107
GPT teacher head0.401
Teacher spread0.294 · 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

Citations89
Published2011
Admission routes2
Has abstractyes

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