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Record W2067004683 · doi:10.1177/0898264312464498

Spousal Loss and Health in Late Life

2012· article· en· W2067004683 on OpenAlexaff
Aniruddha Das

Bibliographic record

VenueJournal of Aging and Health · 2012
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsMcGill University
FundersNutrition Obesity Research Center, University of North CarolinaNational Institute on AgingNational Institutes of Health
KeywordsVulnerability (computing)Mental healthGerontologyPsychologyMedicineDemographyPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: This study queries the linkage of older adults' spousal loss to multiple dimensions of their health. METHODS: Data are from the 2005-2006 National Social Life, Health, and Aging Project, nationally representative of U.S. adults ages 57 to 85. Analyses examine associations of spousal loss and time since loss with multiple health dimensions. RESULTS: Spousal loss is linked to a system of mental, social, behavioral, and biological issues, consistent with a stress-induced weathering process. Biological problems are more uniformly associated with women's than men's loss. While emotional sequelae may partially subside with time, a range of other outcomes remain worse even among individuals a decade or more past loss, than those with current partners. DISCUSSION: Older adults' spousal loss influences multiple dimensions of their health. Gender differences in biological linkages suggest women's greater physiological vulnerability to this weathering event. Effects of loss are long term rather than transient, especially with biological conditions.

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)0.004
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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.079
GPT teacher head0.427
Teacher spread0.348 · 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

Citations86
Published2012
Admission routes1
Has abstractyes

Explore more

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