Spousal protective buffering and type 2 diabetes outcomes.
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".