The effect of social support on the subjective well-being of the elderly in the post-epidemic era: A chain mediation model
Bibliographic record
Abstract
This study was conducted to investigate the influence of perceived social support on subjective well-being and its relationship with meaning in life and resilience among elderly population in China. The Social Support Scale, the 10-item Connor-Davidson Resilience Scale, the Meaning in Life Questionnaire, and the Memorial University of Newfoundland Scale of Happiness were employed. A total of 308 valid questionnaires were collected with a response rate of 100%. The data were analysed using SPSS 27.0. The results were as follows: (1) There were no significant differences in perceived social support, meaning in life, resilience, and subjective well-being by gender. However, significant differences were found in place of origin (urban/rural), socioeconomic status, educational level, marital status, and COVID-19 infection status; (2) Perceived social support, meaning in life, and resilience were positively and significantly correlated with subjective well-being; (3) Meaning in life and resilience mediated the relationship between perceived social support and subjective well-being. Overall, the subjective well-being of the elderly participants was in the medium to high range. Improving perceived social support, meaning in life, and resilience may enhance the well-being of elderly population in the aftermath of major crises such as epidemics.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".