Inequality in the health services utilization in rural and urban china
Bibliographic record
Abstract
BACKGROUND: Inequality in health and health care remains a rather challenging issue in China, existing both in rural and urban area, and between rural and urban. This study used nationally representative data to assess inequality in both rural and urban China separately and to identify socioeconomic factors that may contribute to this inequality. METHODS: This study used 2008 National Health Services Survey data. Demographic characteristics, income, health status, medical service utilization, and medical expenses were collected. Horizontal inequality analysis was performed using nonlinear regression method. RESULTS: Positive inequity in outpatient services and inpatient service was evident in both rural and urban area of China. Greater inequity of outpatient service use in urban than that in rural areas was evident (horizontal inequity index [HI] = 0.085 vs 0.029). In contrast, rural areas had greater inequity of inpatient service use compared to urban areas (HI = 0.21 vs 0.16). The decomposition analysis found that the household income made the greatest pro-rich contribution in both rural and urban China. However, chronic diseases and aging were also important contributors to the inequality in rural area. CONCLUSION: The inequality in health service in both rural and urban China was mainly attributed to the household income. In addition, chronic disease and aging were associated with inequality in rural population. Those findings provide evidences for policymaker to develop a sustainable social welfare system in China.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".