Association between medical insurance and life satisfaction among middle-aged and older adults in China: the mediating role of depression
Bibliographic record
Abstract
BACKGROUND: While studies have reported a positive association between medical insurance and life satisfaction, there is a lack of studies assessing the underlying impact mechanism. The present study aims to investigate the association between Urban and Rural Resident Basic Medical Insurance (URRBMI) and life satisfaction in China, focusing on the mediating role of depression. METHODS: Using 2018 wave of China Health and Retirement Longitudinal Study, we employed ordered logit regression models to examine the correlation between URRBMI and life satisfaction. Causal mediation analysis was used to analyze the mediating effect of depression on this association. RESULTS: URRBMI participation was related to greater life satisfaction (p < 0.01). Depression mediated the correlation between URRBMI and life satisfaction, and the percentage of total effect mediated was 18.20%. DISCUSSION: Middle-aged and older adults covered by URRBMI were more likely to have greater life satisfaction than their counterparts because insurance relieved depression. CONCLUSION: Our study highlighted many policy suggestions, such as improving its coverage, establishing a unified information platform, and mobilizing social forces to provide better life services.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".