Evaluative coping, emotional distress, and adherence in couples with type 2 diabetes.
Bibliographic record
Abstract
INTRODUCTION: Spousal support is one of the strongest and most consistent predictors of Type 2 diabetes treatment adherence. However, the effects of both spouses' evaluations of dyadic coping on emotional distress and patients' physical health remain largely unknown. METHOD: Dyadic data from 117 married couples in which one member is diagnosed with Type 2 diabetes were evaluated in two separate models to explore the associations between (a) patients' and spouses' depression symptoms and patients' adherence to dietary and exercise regimens, and (b) patients' and spouses' acute stress levels and patients' adherence to dietary and exercise regimens. Finally, evaluative dyadic coping was included as a possible moderator between these associations. RESULTS: Results from an actor-partner interdependence model revealed significant actor effects of patients' depression symptoms on patients' adherence to dietary and exercise regimens. Spouses' evaluation of dyadic coping attenuated the direct paths between spouses' depression symptoms and patients' adherence to dietary regimens. No direct pathways were found from patients' or spouses' acute stress to patients' adherence to dietary and exercise regimens. However, spouses' evaluation of dyadic coping attenuated the direct paths between spouses' acute stress and patients' adherence to dietary regimens. DISCUSSION: Tapping into spouses' evaluations of dyadic coping has significant implications for patients' diabetes health outcomes (e.g., adherence to dietary and exercise treatment regimens). Findings from this study highlight the need for systemic interventions targeting both partners. (PsycINFO Database Record
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".