Evidence map›Paper›PMID 40316980›Full record

ArticleBMC public health2025

Why do rural residents in China withdraw from the health insurance system? -A qualitative study based on Y County in S Province, China.

Ying Zuo, Guang Yang, Haoxuan Cheng, Lufa Zhang

Abstract read
In one paragraph

Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ying ZuoSchool of International and Public Affairs, Shanghai Jiao Tong University, No. 1954, Huashan Road, Xuhui District, Shanghai, 200030, China.
Guang YangSchool of International and Public Affairs, Shanghai Jiao Tong University, No. 1954, Huashan Road, Xuhui District, Shanghai, 200030, China.
Haoxuan ChengSchool of International and Public Affairs, Shanghai Jiao Tong University, No. 1954, Huashan Road, Xuhui District, Shanghai, 200030, China.
Lufa ZhangSchool of International and Public Affairs, Shanghai Jiao Tong University, No. 1954, Huashan Road, Xuhui District, Shanghai, 200030, China. zhanglf@sjtu.edu.cn.

Funding

National Social Science Fund of China 22AZD082
6 · The paper itself

Abstract

backgroundSince the 2009 healthcare reform, the Chinese government has been committed to establishing a universal healthcare system. This study aims to investigate the complex reasons behind rural residents' decision to forgo participation in the medical insurance system by examining causal relationships and interactions.

methodsThis study employs content analysis, conducting in-depth interviews with 42 stakeholders, including government administration departments, tax departments, medical insurance departments, village committees, and residents who have withdrawn from the health insurance system.

resultsThe study finds that the primary reasons rural residents withdraw from the medical insurance system are fourfold: heavy economic burdens, unfair design of the medical insurance system, failure of conventional mobilization strategies, and the internet's impact on residents' cognitive biases.

conclusionThe decision of rural residents in China to withdraw from the medical insurance system is influenced not only by economic factors but also by the system's design and various aspects of rural governance. Moving forward, the Chinese government should optimize the medical insurance system's design, implement more flexible mobilization and persuasion strategies, and prioritize the identification and regulation of misleading information.

Indexed as

Insurance, HealthRural PopulationAdultChinaFemaleHealth Care ReformHumansInterviews as TopicMaleQualitative ResearchChina's urban-rural residents' medical insurance systemContent analysis methodsRural residents

Identifiers

PMID40316980
PMCPMC12046961

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.