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Record W2894764913 · doi:10.1111/jomf.12534

Caregiving and Union Instability in Middle and Later Life

2018· article· en· W2894764913 on OpenAlexafffundabout
Margaret J. Penning, Zheng Wu

Bibliographic record

VenueJournal of Marriage and the Family · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Dynamics and Relationships
Canadian institutionsSimon Fraser UniversityUniversity of Victoria
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCohabitationDemographyPsychologyMarital statusPopulationStressorGerontologyPolitical scienceMedicineClinical psychologySociology

Abstract

fetched live from OpenAlex

Objective: This study addressed the implications of informal caregiving for subsequent union instability in middle and later life, focusing on associations between gender and union type (marital, cohabiting) and the risk of separation or divorce within heterosexual unions. Background: Although caregiving is widely reported to be stressful and to have negative implications for the health and well‐being of individual caregivers, its implications for union stability are less clear. This is particularly evident with regard to middle‐aged and older adults in both marital and cohabiting unions. Yet caregiving may increase separation or divorce risk by operating as a stressor on the marital or union relationship, leading to lower union quality and subsequent dissolution. Method: Using retrospective data on union histories for a national population‐based cohort of married or cohabiting Canadian men and women aged 45 years and older ( N = 17,194), Cox proportional hazard models were used to examine the main and interactive effects of caregiving history, union type, and gender on union dissolution from age 45 to the time of the survey. Results: Caregiving was more destabilizing when it involved cohabiting rather than marital unions and female rather than male caregivers. Conclusion: These findings suggest that caregiving is associated with union dissolution but that the type of union and gender need to be considered.

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)0.000
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 score0.153
Threshold uncertainty score0.210

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.027
GPT teacher head0.260
Teacher spread0.233 · 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

Citations13
Published2018
Admission routes3
Has abstractyes

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