The Mediating Role of Depression in the Effect of Psychological Well-Being on the Self-Rated Health and Quality of Life of Older Adults: Cross-Sectional Study
Bibliographic record
Abstract
Background: A growing body of evidence has identified that people's physical health could influence self-rated health and quality of life (QoL). However, only focusing on physical health is not adequate for the well-being of older adults. Studies focusing on the impact of psychological well-being on self-rated health and QoL are still rare. Objective: This study aimed to identify the mediating effect of depression on the association between psychological well-being and self-rated health and QoL to comprehensively understand the relationship between them. Methods: We used a cross-sectional study design and secondary data analysis from the Chinese Longitudinal Healthy Longevity Survey of 2017 to 2018. Path analysis was applied to examine the research questions. Results: A large sample of 8839 older adults was included. Among them, more positive affect was found among those who were younger and had more years of schooling, higher household income, greater social security and social insurance, lower depression levels, and higher self-rated health levels. Depression had a partial mediation effect of psychological well-being on self-rated health and QoL, which explained 36% of the total variance (R2=0.36). In addition, psychological well-being had a statistically significant direct effect on self-rated health and QoL (β=0.290; P<.001). Conclusions: Our results indicate that psychological well-being had both direct and indirect effects on self-rated health and QoL. Depression was an important mediator that regulated the effect pathway in older adults.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".