Bibliographic record
Abstract
Access to health care, as a determinant of health, became unequally distributed in China as a result of continued pursuit of the market-oriented reforms introduced the late 1970s. Increasing urban-rural socio-economic disparities jeopardized the equity in social welfare and particularly the equity of access to health care, which has been widely considered a key objective of health care policies, in turn putting the disadvantaged communities in especially vulnerable position by increasing their health risks. Using the household survey data from the China Health and Nutrition Survey (CHNS), changes in the accessibility of health care are herein examined at the household level across socioeconomic groups in nine provinces of China over the 1989-2004 period. First, we describe the access to health care over the 15 years of survey, and then using a multivariate regression we analyze the determinants of access in 2004. We find that the geographic accessibility gap between hospitals and clinics has decreased from 1989 to 2004, while the gap in financial accessibility has increased, making urban hospitals the least accessible facilities. This study concludes a strong correlation between the geographic factors (method of travel, region, and availability of infrastructure in the province of residence) and the quality of access received. Overall, patients who are able to reach the facilities on foot enjoy better access than those who can not. Similarly, residents of the West along with the provinces with higher availability of health care facilities also tend to have a better potential access than their counterparts. Understanding of health care utilization patterns and access issues is essential for informing public decision- and policy-making aimed at improving living conditions and building a harmonious society.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".