PSYCHOLOGICAL VULNERABILITY TO WIDOWHOOD: FINANCIAL STRAIN, WORRY ABOUT CARE, AND SOCIAL ENGAGEMENT
Bibliographic record
Abstract
Objective. This study examines the ways in which the financial, social, and care factors (financial strain, worry about having no caregiver, and social engagement) modify the association between widowhood and depressive symptoms among older adults in China. Methods. Using national representative data from older adults in China in 2000 (n = 15,115), Multiple regressions were used in this study to examine the moderating effects of the financial, social and care factors on the association between widowhood and depressive symptoms. We also performed structural equation modeling to test the mediating effects of these factors on such association. Results. Compared to their married counterpart, widowed older adults show significantly higher level of depressive symptoms (b = 0.46, p < .001), higher percentage of worry about having no caregiver (b = 0.11, p < .001) and financial strain (b = 0.10, p < .01), and lower level of social engagement (b = -0.07, p < .01) which results in higher levels of depressive symptoms. Worry about having no caregiver (b = 0.15, p < .001) significantly moderates the relations between widowhood and depression. Discussion. This study extends bereavement and psychological wellbeing research to developing nations by examining several pathways between bereavement and depressive symptoms. The results provide important implications for intervention when working with widowed older adults in China.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".