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Record W2118602577

Healthcare-financing reforms in transitional society: a Shanghai experience.

2003· article· en· W2118602577 on OpenAlexaff
Weizhen Dong

Bibliographic record

VenuePubMed · 2003
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsUniversity of TorontoCanadian Institutes of Health Research
Fundersnot available
KeywordsHealth careGovernment (linguistics)BusinessCost sharingChinaEconomic growthHealth policyHealth care reformPublic economicsEconomicsPolitical scienceMedicineNursing
DOInot available

Abstract

fetched live from OpenAlex

Since the 1950s, China has had a very wide coverage of healthcare service at the local level. In urban areas, the employment-based healthcare-insurance schemes (Government Insurance Scheme and Labour Insurance Scheme) worked hand in hand with the full employment policy of the Government, which guaranteed basic care for almost every urban resident. However, since the economic reforms of the early 1980s, China's healthcare system has met great challenges. Some came from the reform of the labour system, and other challenges came from the introduction of market forces in the healthcare sector. The new policy of the Chinese Government on the Urban Employees' Basic Health Care Insurance is to introduce a cost-sharing plan in urban China. Like other major social policy changes, this new health policy also has a great impact on the lives of the Chinese people. Affordability has been the major concern among urban residents. Shanghai implemented the cost-sharing healthcare policy in the spring of 2001. It may be too early to assess the pros and cons of the new policy, but evidence shows that the employment-based health-insurance scheme excludes those at high risk and in most need. It is argued that the cost-sharing healthcare system will limit access by some people, especially those who are most vulnerable to the consequences of ill health and those in low-income groups, unless the deductibles vary according to income and unless low-income groups are exempt from paying premiums and deductibles.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.601
Threshold uncertainty score0.506

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.055
GPT teacher head0.237
Teacher spread0.182 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2003
Admission routes1
Has abstractyes

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