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Record W1834903035 · doi:10.5430/ijh.v1n1p32

Does the New Rural Cooperative Medical System with higher reimbursement rates reduce catastrophic health expenditures in rural China?

2015· article· en· W1834903035 on OpenAlexaff
Daijun Zhao, Peter C. Coyte

Bibliographic record

VenueInternational Journal of Healthcare · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsUniversity of Toronto
FundersMinistry of Education of the People's Republic of China
KeywordsReimbursementCatastrophic illnessChinaPaymentEnvironmental healthBusinessMedicineHealth careEconomic growthEconomicsGeographyFinance

Abstract

fetched live from OpenAlex

We used recently available data from the China Health and Nutrition Survey (CHNS) from 2004 to 2011 to re-examine the impact of the New Rural Cooperative Medical System (NRCMS) in the People’s Republic of China on exposure to catastrophic health expenditures. We found that the NRCMS with universal coverage and higher reimbursement rates in 2011 did not reduce the incidence and intensity of out-of-pocket catastrophic payments. Moreover, there were important distributional implications with the poorest protected to the least extent. As such, we suggest policymakers should consider using the NRCMS to shelter rural residents, especially the poorest, from catastrophic medical expenses.

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)0.002
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 score0.494
Threshold uncertainty score0.952

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.032
GPT teacher head0.317
Teacher spread0.285 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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