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Record W2163605903 · doi:10.1177/1010539514565446

Socioeconomic Inequities in Health Care Utilization in China

2015· article· en· W2163605903 on OpenAlexaff
Xin Zhang, Qunhong Wu, Yongxiang Shao, Wenqi Fu, Guoxiang Liu, Peter C. Coyte

Bibliographic record

VenueAsia Pacific Journal of Public Health · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSocioeconomic statusHealth careHealth equityEnvironmental healthContext (archaeology)ChinaCornerstoneHealth policySocial determinants of healthIndex (typography)BusinessMedicineEconomic growthGeographyEconomicsPopulation

Abstract

fetched live from OpenAlex

The study assessed the present degree of inequity in health care utilization as well as the contributions of the main determinants in the context of expending health insurance coverage in China. Data were obtained from the 2008 National Health Services Survey (NHSS) in China. A concentration index was used to quantify the degree of income-related inequity in health care utilization. The need-standardized concentration indexes of outpatient care and inpatient care were 0.015 and 0.197, respectively. Income made the largest contribution to inequity favoring the better-off in the use of health care. The impacts of health insurance schemes on overall inequity varied according to the insurance memberships as well as types of services. The study revealed a pro-rich distribution of the probability of health care across income groups in China. Increased financial protection ability of medical insurance system remains a vital cornerstone to tackle the health care utilization inequity.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.205

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.129
GPT teacher head0.318
Teacher spread0.189 · 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 source (direct Gemma or distilled Codex), 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

Citations70
Published2015
Admission routes1
Has abstractyes

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