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Record W2119506613

Social support and mortality in seniors.

2003· article· en· W2119506613 on OpenAlexaffabout
Kathryn Wilkins

Bibliographic record

VenuePubMed · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsStatistics Canada
Fundersnot available
KeywordsSocial supportMarital statusDemographyGerontologyMental healthPsychologyPopulationMedicineSocial psychologyPsychiatrySociology
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVES: This article investigates the effect of social support on mortality among Canadian seniors. DATA SOURCE: The analysis is based on longitudinal household data from the National Population Health Survey (NPHS) for 2,422 people aged 65 or older in 1994/95. Vital status and date of death were established using data collected in 2000/01. ANALYTICAL TECHNIQUES: Multivariate proportional hazards models were used to study associations between four indicators of social support (marital status; social contacts; participation in organizations; and perceived emotional support) in 1994/95 and death by 2000/01. Separate analyses were performed for men and women. MAIN RESULTS: When the influence of age, socio-economic status, stress, health-related behaviours and physical/mental health status was taken into account, no association between social support and mortality emerged for women, but such a relationship was evident for men. Married men had a 40% lower hazard of death, compared with their non-married counterparts. Participation in organizations also conferred a reduced likelihood of dying for men.

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)0.000
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 score0.408
Threshold uncertainty score0.811

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.058
GPT teacher head0.346
Teacher spread0.288 · 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

Citations38
Published2003
Admission routes2
Has abstractyes

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