Economics Analysis on China’s New Rural Cooperative Medical Scheme: for Example, Chao-yang District in Beijing
Bibliographic record
Abstract
This paper uses economic theory to analyze China’s New Rural Cooperative Medical Scheme, and with empirical research, finds that there are still some problems that need urgent solution in the scheme. And at last the paper recommends: medical service providers should be more competitive, third party to purchase should be executed and improve the regional to enhance the medical funds balance ability. Key words: New Rural Cooperative Medical Scheme, Economic theory, Fairness and Efficiency, Chao-yang District, Beijing Resume: L’article present analyse, avec la theorie de l’economie, le systeme de la nouvelle cooperative medicale rurale et trouve, sur la base des recherches des faits, qu’il existe des problemes dans la nouvelle cooperative medicale rurale mise en application de notre pays. L’article propose enfin des conseils sur l’introduction du mecanisme competitif de l’institution fournisseuse des services medicaux, l’introduction du mecanisme d’achat de la troisieme partie, l’elargissement du domaine de planification et le renforcement de la capacite de regularisation des fonds. Mots-cles: nouvelle cooperative medicale rurale, theorie de l’economie, equite et efficacite, arrondissement Chaoyang 摘要：本文運用經濟學理論對新型農村合作醫療制度進行分析，並結合實證研究，發現我國目前實施的新型農村合作醫療還存在一些亟待解決的問題。本文最後提出了引入醫療服務提供機構的競爭機制，引入第三方購買機制和擴大統籌區域增強基金調劑能力的政策建議。 關鍵詞：新型農村合作醫療；經濟學理論；公平與效率；北京市朝陽區
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".