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Record W2138911402 · doi:10.1037/hea0000054

Spousal protective buffering and type 2 diabetes outcomes.

2014· article· en· W2138911402 on OpenAlexaff
Matthew D. Johnson, Jared R. Anderson, Ann F. Walker, Allison R. Wilcox, Virginia L. Lewis, David C. Robbins

Bibliographic record

VenueHealth Psychology · 2014
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsType 2 diabetesDistressCoping (psychology)Diabetes mellitusMedicineDiabetes managementClinical psychologyPsychologyPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: Guided by the developmental-contextual model of couples coping with chronic illness (Berg & Upchurch, 2007), the purpose of this study is to explore moderated associations between spousal protective buffering and illness outcomes for partners diagnosed with type 2 diabetes (dietary adherence, frequency of exercise, and HbA1c level). Patient diabetes appraisals (distress, control, self-efficacy, and consequences) were explored as potential moderators. METHODS: Participants were 117 married couples in which one member had been diagnosed with type 2 diabetes. Data were gathered from spouses and patients through a survey instrument and analyzed with path analysis. RESULTS: Protective buffering was associated with fewer days of exercise when patients reported low diabetes distress and diabetes consequences. Additionally, protective buffering was associated with higher HbA1c when patients reported high diabetes control. CONCLUSIONS: Protective buffering did not exhibit a uniform association with the type 2 diabetes outcomes. Rather, the association between spousal protective buffering and patient illness adjustment was dependent on patient appraisal of the illness. These findings contribute a nuanced addition to the literature documenting the role of couple coping in chronic illness management and also provide impetus for further, longitudinal investigation of the ways healthy spouses cope with partner illness.

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 score0.066
Threshold uncertainty score0.501

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)0.0000.000

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 head0.039
GPT teacher head0.469
Teacher spread0.430 · 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

Citations15
Published2014
Admission routes1
Has abstractyes

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