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Record W2981619855 · doi:10.1186/s12889-019-7761-6

Health care inequality under different medical insurance schemes in a socioeconomically underdeveloped region of China: a propensity score matching analysis

2019· article· en· W2981619855 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

VenueBMC Public Health · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsSimon Fraser University
FundersNatural Science Foundation of Liaoning Province
KeywordsMedicinePropensity score matchingInequalityHealth carePublic healthBiostatisticsMatching (statistics)Ambulatory careChinaEnvironmental healthEconomic growthNursingEconomicsGeography

Abstract

fetched live from OpenAlex

BACKGROUND: Since economic inequality is often accompanied by health inequalities, health care inequalities are increasingly becoming a hot issue on a global scale. As a developing country, China is still facing the same problems as other countries in the world. Especially in underdeveloped regions, owing to the relatively backward economy, health care inequality may be more serious. The objective of this study was to explore health care inequality in a socioeconomically underdeveloped city, thus providing a certain theoretical basis for further development and reform of the medical insurance schemes. METHODS: We mainly extracted relevant insurance information of 628,952 insured enrollees, as well as consumption of outpatient visit and hospitalization. The propensity score matching had been used to estimate different urban medical insurance schemes effect on healthcare utilization, the choice of hospital types and healthcare cost. RESULTS: Insured enrollees spent most hospitalization expenses in tertiary-level hospitals, which had lowest hospitalization compensation ratios. Healthcare utilization and cost vary significantly by different insurance schemes. Urban employees had significantly higher outpatient visit rates in all hospital types than urban residents. Urban employees preferred to receive hospitalization treatment in tertiary-level hospitals, while those who receive hospitalization treatment in first-level hospitals are more likely to be enrolled in Urban Residents Basic Medical Insurance. Hospitalization expenses and hospitalization compensation ratios of urban employees were also significantly higher than urban residents in all hospital types. CONCLUSIONS: Health care inequality is mainly reflected in the imbalance between hospitalization expenses and hospitalization compensation ratios, as well as inequalities under different medical insurance schemes in healthcare utilization, the choice of hospital types and healthcare cost in socioeconomically underdeveloped regions of China. We should conduct a targeted medical insurance reform for the socioeconomically underdeveloped regions, rather than applying templates of ordinary regions. Further efforts are needed in the future to provide equal health care for every patient.

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)0.004
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 score0.036
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.103
GPT teacher head0.306
Teacher spread0.202 · 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