The Structure and Effectiveness of Health Systems: Exploring the Impact of System Integration in Rural China
Bibliographic record
Abstract
INTRODUCTION: Facing the challenges of aging populations, increasing chronic diseases prevalence and health system fragmentation, there have been several pilots of integrated health systems in China. But little is known about their structure, mechanism and effectiveness. The aim of this paper is to analyze health system integration and develop recommendations for achieving integration. METHOD: Huangzhong and Hualong counties in Qinghai province were studied as study sites, with only Huangzhong having implemented health system integration. Questionnaires, interviews, and health insurance records were sources of data. Social network analysis was employed to analyze integration, through structure measurement and effectiveness evaluation. RESULTS: Health system integration in Huangzhong is higher than in Hualong, so is system effectiveness. The patient referral network in Hualong has more "leapfrog" referrals. The information sharing networks in both counties are larger than the other types of networks. The average distance in the joint training network of Huangzhong is less than in Hualong. Meanwhile, there are deficiencies common to both systems. CONCLUSION: Both county health systems have strengths and limitations regarding system integration. The use of medical consortia in Huangzhong has contributed to system effectiveness. Future research might consider alternative more context specific models of health system integration.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".