Inequities in Access: The Impact of a Segmented Health Insurance System on Physician Visits and Hospital Admissions Among Older Adults in the 2014 China Family Panel Studies
Bibliographic record
Abstract
The fragmentation of job-based and community-based insurance plans inevitably undermines health care accessibility in China’s market-oriented health system, especially for uninsured and rural residents. Based on the 2014 China Family Panel Studies, this secondary data analysis examined whether socioeconomic indicators, health-related determinants, and particularly social health insurance status affect physician visits in the past 2 weeks and hospital admissions in the past 12 months among a representative sample of older adults (n = 6,570). Grounded in Andersen’s behavioral framework, 2 series of logistic regression analyses were performed: one was built in a hierarchical manner, assessing blocks of predisposing, enabling, health-need, and lifestyle-behavioral factors; the other was conducted in a cross-referencing manner, comparing uninsured populations with job-based and community-based insurance enrollees. Results show that, after full adjustment, the odds of physician visits were lower among urban insurance enrollees (OR = 0.67, 95% CI: 0.47–0.97) than rural residents. For hospital admissions, both uninsured elders (OR = 0.65, 95% CI: 0.48–0.87) and community-based insurance enrollees (OR = 0.67, 95% CI: 0.47–0.97) had lower use of inpatient care than job-based insurance enrollees, demonstrating inequitable access. This study suggests that policy efforts should unify the social health insurance system to combat existing insurance-related inequities in health care use for underserved aging populations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
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".