Dual assurance for healthcare and future education development

Normalized assistance for low-income population in rural areas—evidence from the population identification

Bibliographic Data

ID22087391
AuthorsXiaoyan Peng (0000-0001-5874-6233, Sun Yat-sen University), Yanzhao Zeng (0000-0002-2658-9197, Guangzhou University), Yanrui Chen (Guangzhou University), Huaxing Wang (0000-0001-5227-1994, Hangzhou City University, corresponding author)
Year2024
Volume12
Pages1384474-1384474
Publication date2024-11-19
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2024.1384474
PMID39628808
OpenAlexW4404509531
LanguageEN
References cited34

Introduction: City in Guangdong Province as the focal area. Addressing both healthcare and educational concerns, this research seeks to provide insights that can guide policy and support for this demographic. Methods: Utilizing big data analysis and deep learning algorithms, a targeted intelligent identification classification model was developed to accurately detect and classify rural low-income individuals. Additionally, a questionnaire survey methodology was employed to separately investigate healthcare and future education dimensions among the identified population. Results: The proposed model achieved a population identification accuracy of 91.93%, surpassing other baseline neural network algorithms by at least 2.65%. Survey results indicated low satisfaction levels in healthcare areas, including medical resource distribution, medication costs, and access to basic medical facilities, with satisfaction rates below 50%. Regarding future education, issues such as tuition burdens, educational opportunity disparities, and accessibility challenges highlighted the concerns of rural low-income families. Discussion: The high accuracy of the model demonstrates its potential for precise identification and classification of low-income populations. Insights derived from healthcare and education surveys reveal systemic issues affecting satisfaction and accessibility. This research thus provides a valuable foundation for future studies and policy development targeting rural low-income populations in healthcare and education

Business · Economic growth · Economics · Environmental health · Health care · Low income · Population · Rural area · Rural population · Socioeconomics · Advanced Technologies in Various Fields · Cardiovascular Health and Risk Factors · Healthcare Systems and Reforms · Medicine

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Open DOIOpen Access