A long and resilient life: the role of coping strategies and variability in their use in lifespan among women
Bibliographic record
Abstract
OBJECTIVES: Associations of stress-related coping strategies with lifespan among the general population are understudied. Coping strategies are characterized as being either adaptive or maladaptive, but it is unknown the degree to which variability in tailoring their implementation to different contexts may influence lifespan. METHOD: = 47) completed a validated coping inventory and reported covariate information in 2001. Eight individual coping strategies (e.g., Acceptance, Denial) were considered separately. Using a standard deviation-based algorithm, participants were also classified as having lower, moderate, or greater variability in their use of these strategies. Deaths were ascertained until 2019. Accelerated failure time models estimated percent changes and 95% confidence intervals (CI) in predicted lifespan associated with coping predictors. RESULTS: In multivariable models, most adaptive and maladaptive strategies were associated with longer and shorter lifespans, respectively (e.g., per 1-SD increase: Active Coping = 4.09%, 95%CI = 1.83%, 6.41%; Behavioral Disengagement = -6.56%, 95%CI = -8.37%, -4.72%). Moderate and greater (versus lower) variability levels were similarly and significantly related to 8-10% longer lifespans. Associations were similar across age, racial/ethnic, residential income, and marital status subgroups. CONCLUSIONS: Findings confirm the adaptive and maladaptive nature of specific coping strategies, and further suggest benefits from both moderate and greater variability in their use for lifespan among women.
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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 | 0.006 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".