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Record W2492436946 · doi:10.1037/hea0000395

Dyadic coping and inflammation in the context of chronic stress.

2016· article· en· W2492436946 on OpenAlexafffund
Jean‐Philippe Gouin, Sabrina Scarcello, Chelsea da Estrela, Chantal Paquin, Erin T. Barker

Bibliographic record

VenueHealth Psychology · 2016
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsConcordia University
FundersSocial Sciences and Humanities Research Council of CanadaConcordia University
KeywordsCoping (psychology)PsycINFOPsychologyDistressStressorClinical psychologySocial supportInterpersonal communicationDevelopmental psychologySocial psychologyMEDLINE

Abstract

fetched live from OpenAlex

OBJECTIVE: Marital quality impacts inflammatory processes. Dyadic coping, a spousal support process in which members of a couple work together to cope with the stressors that 1 or both partners are facing, is associated with higher marital satisfaction and reduced psychological distress. The goal of the present study was to evaluate whether dyadic coping is also related to systemic inflammation among individuals facing chronic parenting stress. METHOD: Forty-four parents of children with an autism spectrum disorder completed self-report questionnaires on dyadic coping, marital satisfaction, perceived social support, and caregiving burden. They also provided a blood sample for C-reactive protein (CRP) analysis, a marker of systemic inflammation. RESULTS: Higher positive dyadic coping was significantly associated with lower circulating CRP, while negative dyadic coping was unrelated to CRP. After adjusting for individual differences in marital satisfaction, perceived social support, and caregiving burden, positive dyadic coping became marginally associated with CRP. CONCLUSION: Positive dyadic coping is a specific interpersonal process that may modulate systemic inflammation among individuals exposed to chronic stress. (PsycINFO Database Record

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
Threshold uncertainty score0.725

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.087
GPT teacher head0.458
Teacher spread0.371 · 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

Citations33
Published2016
Admission routes2
Has abstractyes

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