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ORIGINAL RESEARCH article

Front. Public Health
Sec. Health Economics
Volume 12 - 2024 | doi: 10.3389/fpubh.2024.1384474
This article is part of the Research Topic Multilevel Medical Security Systems and Big Data in Healthcare: Trends and Developments, Volume II View all 4 articles

Dual Assurance for Healthcare and Future Education Development: Normalized Assistance for Low-Income Population in Rural Areas-Evidence from the Population Identification

Provisionally accepted
  • 1 School of Government, Sun Yat-sen University, Guangzhou, Guangdong Province, China
  • 2 School of Economics and Statistics, Guangzhou University, Guangzhou, Guangdong Province, China
  • 3 School of Public Administration, Guangzhou University, Guangzhou, Guangdong Province, China
  • 4 Law School, Hangzhou City University, Hangzhou, Jiangsu Province, China
  • 5 Zhejiang University, Hangzhou, China
  • 6 Digital Rural Research Center, Hangzhou City University, Hangzhou, China

The final, formatted version of the article will be published soon.

    This work aims to delve into the correlation between healthcare and future education for the rural low-income population to improve support for this demographic. This work takes J City in Guangdong Province as the research area. Big data analysis and deep learning algorithms are introduced to design a targeted population intelligent identification classification model to accurately identify rural low-income individuals. Through survey methods, this work analyzes both healthcare and future education aspects. The questionnaire survey methodology is utilized to conduct separate investigations and analyses on healthcare and future education. The results reveal that the identification accuracy of the targeted population recognition mechanism reaches 91.93%, which is at least 2.65% higher in accuracy compared to other baseline neural network algorithms. This validates the significant advantage of the model in the accuracy of identifying and classifying low-income populations. Analysis of the questionnaire survey results indicates that in terms of healthcare, including the distribution of medical resources, medication costs, and satisfaction with basic medical facilities, the satisfaction rates are low and do not exceed 50%. In the realm of future education, diverse perspectives on issues such as tuition burdens, disparities in educational opportunities, and transportation reveal the concerns of the rural low-income population regarding future education.Therefore, this work provides profound insights into the current situation of this specific demographic and serves as a valuable reference foundation for future related studies and policy formulation.

    Keywords: rural low-income population, healthcare, Future Education, target population identification, normalized assistance

    Received: 09 Feb 2024; Accepted: 31 Oct 2024.

    Copyright: © 2024 Peng, Zeng, Chen and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Huaxing Wang, Law School, Hangzhou City University, Hangzhou, Jiangsu Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.