Association between medical insurance and life satisfaction among middle-aged and older adults in China: the mediating role of depression
Classification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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) | 0.000 | 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 it