Association Between Depression and Subjective Well-being in older patients with Chronic Diseases in Rural Ethnic Areas of Qiannan, Guizhou: The Mediating Role of Social Support
Bibliographic record
Abstract
Abstract Objective: To explore the associations among social support, depression, and subjective well-being in older patients with chronic diseases in the rural ethnic regions of Qiannan, Guizhou. Methods: This cross-sectional study enrolled oder patients with chronic diseases in Qiannan, Guizhou between June and September 2022. The social support, depression, and subjective well-being were assessed by a self-developed questionnaire on general information, the social support rating scale (SSRS), the geriatric depression scale (GDS-15), and the Memorial University of Newfoundland Happiness Scale (MUNSH), respectively. Results: A total of 2,156 participants (1,104 males; average age of 71.15±8.04) were included. Their mean SSRS and MUNSH scores were 34.96±7.98 and 27.17±9.48, respectively. The GDS-15 score was 7.23±2.59, with 1529 individuals (70.9%) having depressive symptoms. Multivariate linear regression analysis revealed that mild depression (B = -7.795, 95% CI: -8.437 to -7.153, P < 0.001), moderate to severe depression (B = -11.631, 95% CI: -12.623 to -10.639, P < 0.001), and moderate social support (B = -2.661, 95% CI: -4.063 to -1.259, P < 0.001) were independently associated with subjective well-being. The structural equation model results revealed that the total effect of depression symptoms on subjective well-being in elderly patients with chronic diseases is -0.528, with a direct effect of -0.474 (95% CI: -0.503 to -0.443), accounting for 89.77% of the total effect. The mediating effect of social support on the association between depression and subjective well-being is -0.133 (95% CI: -0.070 to -0.041), constituting 10.23% of the total effect. Conclusions: Older patients with chronic diseases in the rural ethnic areas of Qiannan, Guizhou, exhibited a high prevalence of depressive symptoms and low levels of subjective well-being. Social support partially mediated the association between depression and subjective well-being in this population. Thus, proactive measures are warranted to fortify the social support system for older patients with chronic diseases in Qiannan, Guizhou.
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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 | 0.001 |
| 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".