Has socioeconomic inequality in perceived access to health services narrowed among older adults in China?
Bibliographic record
Abstract
OBJECTIVE: To analyze the degree, evolution and causes of socioeconomic inequality in perceived access to health services among the older adults in China. METHODS: The data used in this study were drawn from the 4 waves of the Chinese Longitudinal Healthy Longevity Survey (CLHLS) in 2008, 2011, 2014, 2018. Erreygers index (EI) was used to measure socioeconomic inequality in perceived access to health services in each survey wave. A panel logit regression model was used to examine the impact of socioeconomic status on perceived access to health services. The recentered influence function (RIF) regression decomposition method was used to explore the causes of socioeconomic inequality in perceived access to health services. Inverse probability weighting (IPW) was employed to adjust estimates for missing responses and loss to follow-up. RESULTS: "Pro-rich" socioeconomic inequality in perceived access to health services in China was found with inequality falling through time. The older adults with higher incomes, who had adequate financial support, and those who were wealthier compared with other residents reported lower socioeconomic inequality in perceived access to health services. Having basic health insurance and access to care resources when ill can help alleviate such inequalities. CONCLUSIONS: Socioeconomic inequality in perceived access to health services was shown to be responsive to policies that enhance health insurance coverage and support the provision of (paid and unpaid) caregiving for the older adults.
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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 | 0.011 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".