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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".