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Record W4401711342 · doi:10.1080/10826084.2024.2392519

Associations Between Divorce Histories and Unhealthy Alcohol Use Among Middle Aged and Older Adults

2024· article· en· W4401711342 on OpenAlexaff
Katherine J. Ford, Rachel Burns

Bibliographic record

VenueSubstance Use & Misuse · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsCarleton University
Fundersnot available
KeywordsPsychologyGerontologyDemographyMiddle ageMedicineDevelopmental psychologySociology

Abstract

fetched live from OpenAlex

Background: Unhealthy alcohol use has been considered a coping strategy related to stressful and traumatic life events such as relationship loss. Yet, the effects of marital status on health behaviors are generally studied cross-sectionally or over one transition. We explored associations between the frequency and quantity of alcohol use with the number of episodes and duration of separation/divorce events across adulthood among English adults in mid to later life. Methods: This study used life history data from wave 3 (2006/07) of the English Longitudinal Study of Aging to compute marital sequences based on marital status at each year of age from 18 years of 6,355 adults aged 50–80 years. These sequences were used to compute the portion of adulthood spent separated/divorced and the number of episodes of divorce. These variables were used as predictors in logistic regressions predicting unhealthy alcohol use, while also controlling for current marital status. Results: We found that the number of episodes of separation/divorce increased the odds of drinking ≥5 days/week and binge drinking (≥6 drinks/occasion for women; ≥8 drinks/occasion for men), whereas the portion of adulthood spent divorced was not associated with drinking frequency or binge drinking. Some nuances by gender were also noted. Conclusions: Recurrent transitions into separation/divorce over adulthood appears to increase risk of unhealthy alcohol use in mid to later life beyond the risks associated with current marital status.

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)0.000
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 score0.154
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.040
GPT teacher head0.293
Teacher spread0.253 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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