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Record W4237476248 · doi:10.1093/geront/gnw162.2035

PERCEIVED SPOUSAL SUPPORT BUFFERS ASSOCIATION BETWEEN DAILY AFFECTIVE REACTIVITY AND HEMOGLOBIN A1C

2016· article· en· W4237476248 on OpenAlexaff
Victoria I. Michalowski, Maureen C. Ashe, Kenneth Madden, Denis Gerstorf, Christiane A. Hoppmann

Bibliographic record

VenueThe Gerontologist · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture Market Analysis Ukraine
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAssociation (psychology)HemoglobinReactivity (psychology)PsychologyClinical psychologyMedicineInternal medicinePsychotherapist

Abstract

fetched live from OpenAlex

Affective reactivity to daily stressors has been linked with poor health outcomes (Piazza, Charles, Sliwinski, Mogle, & Almeida, 2013).Spousal support may represent a protective factor for reactive individuals, particularly in older age when spouses increasingly turn to each other for support.This study uses 21 simultaneous momentary assessments from both partners in 120 older couples (M age = 71 years; M relationship duration = 41 years) and links stressor-negative affect associations with glycosylated hemoglobin (HbA1C), a biomarker of diabetes risk.In line with previous research, initial findings suggest that responding to a social stressor with high negative affect is associated with higher HbA1C.Importantly, this association is moderated by perceived spousal support.Thus, greater perceptions of spousal support may be protective for glycemic control of highly reactive individuals.Taken together, spousal support may represent a resource for attenuating declining glycemic control in older adulthood.

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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.014
GPT teacher head0.218
Teacher spread0.204 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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