The Costs of Coping: Long-Term Mortality Risk in Aging Men
Bibliographic record
Abstract
OBJECTIVES: Prospective associations between coping and all-cause mortality risk are understudied, particularly among nonmedical samples. We assessed independent and joint associations of multiple components of the transactional stress and coping model with all-cause mortality in a cohort of community-dwelling men. We were particularly interested in how coping effort related to mortality. METHODS: Participants included 743 men from the Veterans Affairs Normative Aging Study who completed 1+ stress and coping assessment in 1993-2002 (baseline age: M = 68.4, standard deviation [SD] = 7.1) and had mortality follow-up through 2020. The Brief California Coping Inventory assessed coping with a past-month stressor. Cox regression evaluated associations of problem stressfulness, coping strategies, total coping effort, and coping efficiency with all-cause mortality risk. RESULTS: Over a mean follow-up of 16.7 years (SD = 7.1), 473 (64%) men died. Problem stressfulness was not associated with mortality risk (hazard ratio [HR]: 1.07, 95% confidence interval [CI]: 0.98-1.17), adjusted for demographics and health conditions. When examining coping via specific strategies, only social coping was associated with higher mortality risk (HR: 1.15, 95% CI: 1.05-1.26) after Bonferroni correction. Total coping effort was associated with 14% greater risk of all-cause mortality (95% CI: 1.04-1.26), independent of problem stressfulness, demographics, and health conditions. Coping efficiency, a benefit-cost ratio of coping efficacy to total coping effort, was not associated with mortality risk in adjusted models. DISCUSSION: Total coping effort may be an important indicator for longevity among aging men, above and beyond problem stressfulness and specific coping strategies, which have been the foci in prior research.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".