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Record W3024481457 · doi:10.1037/hea0000883

Relationship quality and 5-year mortality risk.

2020· article· en· W3024481457 on OpenAlexaff
Jamila Bookwala, Trent Gaugler

Bibliographic record

VenueHealth Psychology · 2020
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsToronto Metropolitan University
FundersOffice of Research on Women's HealthOffice of Behavioral and Social Sciences ResearchOffice of AIDS ResearchNational Institute on AgingNational Institutes of Health
KeywordsSpouseDemographyOdds ratioMarital statusLogistic regressionOddsMedicinePsychologyGerontologyPopulationInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The present study examined positive and negative aspects of relationship quality with one's spouse or partner as predictors of mortality and the role of gender in moderating this link. METHOD: = 1,734). Positive aspects of relationship quality (frequency of opening up to the partner to talk about worries and relying on the partner) and negative aspects (frequency of the partner making too many demands and criticism by the partner) were assessed. Survival/mortality status was recorded at the time of Wave 2 data collection 5 years later (1,567 alive; 167 deceased). Covariates included sociodemographic variables, relationship type, health status, and the network size of close family relationships and friendships. RESULTS: = 1.44, 95% CI [1.10, 1.88]). Gender did not moderate the relationship-quality-mortality link. CONCLUSIONS: Negative relationship quality, notably, criticism received from one's spouse or partner, heightens older adults' risk of mortality. These results suggest the value of developing interventions that target reducing expressed criticism in couple relationships. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

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)0.001
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 score0.145
Threshold uncertainty score0.908

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.271
GPT teacher head0.581
Teacher spread0.310 · 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

Citations22
Published2020
Admission routes1
Has abstractyes

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