Are the ways women cope with stressors related to their health behaviors over time?
Bibliographic record
Abstract
OBJECTIVE: Emerging research suggests the use of certain strategies to cope with stressors relate to disease and mortality risk, and lifestyle habits may be underlying mechanisms. Studies show psychological symptoms (eg, anxiety) and states (eg, happiness) predict the likelihood of adopting an integrated lifestyle that encompasses key health-related behaviors, like smoking. Yet, whether psychological processes, including stress-related coping, influence the adoption of a healthy lifestyle is unknown. We investigated whether coping strategies typically deemed adaptive (eg, seeking emotional support) and maladaptive (eg, denial) relate to sustaining a healthy lifestyle over a 16-year follow-up. We also explored whether variability in the use of these strategies, reflecting attempts to find the best strategy for a given stressor, subsequently relates to lifestyle. METHODS: Women (N = 46 067) from the Nurses' Health Study II cohort reported their use of 8 coping strategies in 2001, from which we also derived coping variability levels (lower, moderate, greater). Health behaviors (eg, physical activity, smoking, sleep), self-reported every 4 years from baseline until 2017, were combined into a lifestyle score. Generalized estimating equations, controlling for baseline demographics and health-related factors, were performed. RESULTS: Most adaptive strategies and greater variability levels were associated with a higher likelihood of sustaining a healthy lifestyle (eg, active coping, relative risk [RR] = 1.09, 95% confidence interval [CI], 1.08-1.11), with the reverse evident with maladaptive strategies (eg, behavioral disengagement, RR = 0.94, CI, 0.93-0.95), but some unexpected results also emerged. CONCLUSIONS: Findings highlight the importance of going beyond the usual (mal)adaptive categorization of coping strategies when investigating their predictive value with behavioral outcomes.
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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 | 0.009 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".