Are there sociodemographic-specific associations of coping with heart disease and diabetes incidence?
Bibliographic record
Abstract
OBJECTIVE: Psychological factors, including psychological distress and well-being, have been associated with cardiometabolic disease risk. Here, we examined whether a psychological process, namely how individuals cope with stressors, relates to such risk, which has been understudied. METHOD: During 2004-2006, 2,142 participants without heart disease and diabetes from the Midlife in the U.S. study completed a validated coping inventory assessing six strategies (positive reinterpretation and growth, active coping, planning, focus on and venting of emotion, denial, and behavioral disengagement) and relevant covariates. As a proxy for coping flexibility, participants were also classified as having lower, moderate, or greater variability in their use of these strategies. Heart disease and diabetes were documented in 2013-2015. Logistic regressions modeled adjusted odds ratios (AORs) and 95% confidence intervals (CIs) of developing heart disease and diabetes, separately, with coping exposures. RESULTS: In sociodemographic-adjusted models, greater use of adaptive strategies predicted lower diabetes risk (e.g., positive reinterpretation and growth: AOR = 0.83; 95% CI [0.72, 0.96]); estimates were weaker for maladaptive strategies, and all strategies were unrelated to heart disease. All associations for coping variability were null. In secondary analyses, greater use of adaptive strategies predicted lower heart disease risk in more educated participants only (e.g., active coping: AOR = 0.71; 95% CI [0.55, 0.92]) and lower diabetes risk in females only (e.g., planning: AOR = 0.75; 95% CI [0.61, 0.91]). Results were maintained additionally adjusting for health, behavioral, and social factors. CONCLUSIONS: Findings suggest sex and education differences in coping's association with heart disease and diabetes. Future studies should recognize adaptive strategies may be more potent for health among certain populations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.002 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".