Psychological vulnerability of widowhood: financial strain, social engagement and worry about having no care-giver as mediators and moderators
Bibliographic record
Abstract
ABSTRACT This study examined how financial strain, worry about having no care-giver and social engagement modify the association between widowhood and depressive symptoms among older adults in China. Using national representative data from older adults in China in 2006, we ran structural equation models and ordinary least square regressions to investigate the mediating and moderating effects of financial strain, worry about having no care-giver and social engagement on the association between widowhood and depressive symptoms. All three variables significantly mediated the association between widowhood and depressive symptoms. Compared to their married counterparts, widowed older adults showed more worry about having no care-giver, increased financial strain and lower social engagement, which were significantly associated with depressive symptoms. Higher level of worry about having no care-giver and lower social engagement significantly exacerbated the adverse effects of widowhood on depressive symptoms in the moderation analyses. Our finding of mediating effects suggests that widowhood is negatively related to psychological wellbeing via financial strain, social engagement and care resources. The results regarding moderating effects suggest that alleviating worry about having no care-giver and increasing social engagement may buffer the deleterious effect of widowhood on psychological wellbeing in later life.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".