Psychosocial resources as predictors of resilience and healthy longevity of older widows
Bibliographic record
Abstract
Many factors affect the observed increase of mortality following loss of a spouse. In the research reported in this chapter, we focus on the influence of recently recognized psychosocial resource factors in enhancing the resilience and healthy longevity of older widows. We conducted a 6.5-year longitudinal study of the mortality risk of 385 older widows, who were assessed at baseline on measures of perceived psychosocial resources, health-related self-reports, and psychological traits of challenge and control. A Cox regression analysis of predictor variables was used to examine the mortality risk related to the baseline measures of psychosocial resources and psychological trait measures. Those widows who survived longer were mainly those with higher scores on spiritual resources, and on resources of family stability, social engagement, and commitment to life tasks. In contrast, high scores on control and challenge traits had an unexpected negative effect on longevity. Our findings confirm that psychosocial resource factors have a significant effect on resilience and longevity. Introduction A broad range of factors can influence the mortality of women who have become widows. In the research reported in this chapter, we examine the extent to which key psychosocial resources influence the resilience and healthy longevity of older widows. Widowhood is a common occurrence in the lives of midlife and older women. Almost one half of women over the age of 65 years are widowed (Fields and Casper, 2001). The gerontological literature (e.g.
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".