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Record W2168518137 · doi:10.1093/geronb/gbt104

Widowhood, Age Heterogamy, and Health: The Role of Selection, Marital Quality, and Health Behaviors

2013· article· en· W2168518137 on OpenAlexaff
Kate H. Choi, Sarinnapha M. Vasunilashorn

Bibliographic record

VenueThe Journals of Gerontology Series B · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Dynamics and Relationships
Canadian institutionsWestern University
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsSpouseMental healthDisadvantagedMarital statusPsychologyVulnerability (computing)GerontologyHealth and Retirement StudySocioeconomic statusDemographyMedicinePopulationPsychiatrySociology

Abstract

fetched live from OpenAlex

OBJECTIVE: Although the impact of widowhood on the surviving spouse's health has been widely documented, there is little empirical research examining whether certain spousal choice decisions and marital sorting patterns predispose individuals to be more vulnerable to the adverse consequences of widowhood for health. DESIGN AND METHOD: We use data from the Wisconsin Longitudinal Study and employ ordinary least squares models to (a) document variations in mental and physical health between married and widowed persons, (b) determine whether widowed persons in age heterogamous unions are especially vulnerable to the adverse consequences of widowhood, and (c) investigate to what extent differential selection, marital quality, and health practices account for health disparities by marital status and the spousal age gap. RESULTS: Widowed persons, especially those in age heterogamous unions, have worse mental health than married persons, but they do not seem to be more disadvantaged in terms of physical health. Differential selection, marital quality, and health behaviors partly account for some of the health disparities by marital status and spousal age gap. DISCUSSION: Our findings suggest that marrying a spouse who is very dissimilar in age may enhance one's vulnerability to the adverse consequences of widowhood for health.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.132
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.120
GPT teacher head0.406
Teacher spread0.286 · 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.

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

Citations23
Published2013
Admission routes1
Has abstractyes

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