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Record W4311947574 · doi:10.3389/fpubh.2022.1014351

Health shocks, basic medical insurance and common prosperity: Based on the analysis of rural middle-aged and elderly groups

2022· article· en· W4311947574 on OpenAlexaff
Yuan Zhang, Yuquan Sun, Mingli Xie, Yuping Chen, Shouhui Cao

Bibliographic record

VenueFrontiers in Public Health · 2022
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsUniversity of Guelph
FundersFundamental Research Funds for the Central UniversitiesTrường Đại học Kinh tế - Luật, Đại học Quốc gia Thành phố Hồ Chí MinhNational Social Science Fund of ChinaZhongnan University of Economics and Law
KeywordsProsperityShock (circulatory)ChinaWelfareDemographic economicsRural areaPromotion (chess)PovertyMedicineEconomic growthSocioeconomicsGerontologyEconomicsGeographyPolitical sciencePolitics

Abstract

fetched live from OpenAlex

Health is a major part of human welfare. The index system of common prosperity was constructed for middle-aged and elderly people in rural areas. Besides, the impart of health shocks and rural basic medical insurance on common prosperity was explored. The data for this study came from China Health and Retirement Longitudinal Survey (CHARLS) in 2013, 2015, and 2018. The finding shows that health shocks hindered the improvement of the common prosperity of the middle-aged and elderly in rural areas, among which daily activities produced the greatest negative effect. The heterogeneity analysis shows that health shocks have a stronger negative effect on the common prosperity of low-income groups than that of high-income ones. The shock of daily activity ability has the greatest influence on the middle-aged and elderly between 45 and 55 years old. However, acute health shocks have a strong negative effect on those aged above 56. The mechanism analysis shows that rural basic medical insurance can alleviate the health shocks to middle-aged and elderly people, but the effect is limited. In general, low-income groups benefit more. Therefore, China should speed up the promotion of the Healthy China Strategy and the reform of the rural basic medical insurance system, and prompt changes from an inclusive to a targeted policy to provide more precise safeguards for vulnerable groups.

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)0.016
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.049
GPT teacher head0.362
Teacher spread0.314 · 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.

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

Citations13
Published2022
Admission routes1
Has abstractyes

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