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Record W4319299517 · doi:10.1136/bmjdrc-2022-003080

How sweet is your love? Disentangling the role of marital status and quality on average glycemic levels among adults 50 years and older in the English Longitudinal Study of Ageing

2023· article· en· W4319299517 on OpenAlexaff
Katherine J. Ford, Annie Robitaille

Bibliographic record

VenueBMJ Open Diabetes Research & Care · 2023
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsUniversity of Ottawa
FundersFonds National de la Recherche Luxembourg
KeywordsSpouseMedicineGlycemicLongitudinal studyHealth and Retirement StudyMarital statusGerontologyDemographySocial supportAgeingDiabetes mellitusPsychologyPopulationInternal medicineEnvironmental healthEndocrinology

Abstract

fetched live from OpenAlex

INTRODUCTION: The health benefits of marriage have been widely documented and, to a lesser extent, the effects of marital quality. Marital relationships may be particularly relevant to the health of older adults. This study explores the associations of marital status and marital quality with average glycemic levels in older adults using longitudinal data. RESEARCH DESIGN AND METHODS: Our sample consisted of adults aged 50-89 years without previously diagnosed diabetes from the English Longitudinal Study of Ageing (n=3335). We used biomarker data from waves 2 (2004/2005), 4 (2008/2009) and 6 (2012/2013) to analyze changes in hemoglobin A1c (HbA1c) levels within individuals in relation to their marital indicators (marital status, social support from spouse, and social strain from spouse) over time using linear fixed effect models. RESULTS: We found that being married was associated with lower HbA1c values (β: -0.21%; 95% CI -0.31% to -0.10%) among adults without pre-existing diabetes. Spousal support and spousal strain were generally not associated with HbA1c values. CONCLUSIONS: It seems that marital relationships, regardless of the quality of the relationship, are associated with lower HbA1c values for male and female adults aged over 50 years.

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 score0.158
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.126
GPT teacher head0.483
Teacher spread0.358 · 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

Citations25
Published2023
Admission routes1
Has abstractyes

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