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Record W2163605903 · doi:10.1177/1010539514565446

Socioeconomic Inequities in Health Care Utilization in China

2015· article· en· W2163605903 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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)0.010
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.279
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.129
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