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  1. Article ; Online: Health poverty alleviation in China from the perspective of historical institutionalism: policy changes and driving factors.

    Xu, Li / You, Xiaojian / Cui, Yinan / You, Jiali

    Frontiers in public health

    2024  Volume 11, Page(s) 1265588

    Abstract: Health poverty alleviation is an effective tool for improving the living quality and developmental conditions of impoverished populations. Since 1978, China has been actively implementing health poverty alleviation projects, resulting in a more robust ... ...

    Abstract Health poverty alleviation is an effective tool for improving the living quality and developmental conditions of impoverished populations. Since 1978, China has been actively implementing health poverty alleviation projects, resulting in a more robust rural healthcare service network and increased convenience for the local population to access medical treatment. However, it is crucial to acknowledge that China still faces a complex situation with the simultaneous existence of multiple disease threats and the interweaving of various health influencing factors. Ongoing risks of emerging infectious diseases persist, and some previously controlled or eliminated infectious diseases are at risk of resurgence. The incidence of chronic diseases is on the rise and exhibits a trend toward affecting younger populations. Therefore, examining the successful experiences of China's health poverty alleviation over the past 40 years becomes a critically important issue. The study focuses on China's health poverty alleviation policies, employing historical institutionalism as a theoretical perspective to analyze the historical changes and evolutionary logic of health poverty alleviation policies. A historical institutionalist analytical framework for health poverty alleviation policies is constructed. The research findings reveal that China's health poverty alleviation policy has undergone three distinct periods since 1978: the initial phase (1978-2000), the exploratory phase (2000-2012), and the stable development phase (2013-present). At the macro level, the political, economic, and social contexts of different periods have influenced the evolution of health poverty alleviation policies. On the meso level, coordination effects and adaptive expectations have had an impact on China's health poverty alleviation policy. At the micro level, various actors, including the central government, local governments at different levels, social forces, and impoverished communities, interact during the evolution of health poverty alleviation policies. This paper summarizes the theoretical aspects of China's health poverty alleviation policy experience. The research conclusions, viewed through the lens of historical institutionalism, offer practical insights into the evolution of government policies. This provides directional guidance for enhancing health poverty alleviation projects.
    MeSH term(s) Humans ; Poverty ; China ; Population Dynamics ; Public Policy ; Rural Population
    Language English
    Publishing date 2024-01-17
    Publishing country Switzerland
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2711781-9
    ISSN 2296-2565 ; 2296-2565
    ISSN (online) 2296-2565
    ISSN 2296-2565
    DOI 10.3389/fpubh.2023.1265588
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: One-Stage Shifted Laplacian Refining for Multiple Kernel Clustering.

    You, Jiali / Ren, Zhenwen / Yu, F Richard / You, Xiaojian

    IEEE transactions on neural networks and learning systems

    2023  Volume PP

    Abstract: Graph learning can effectively characterize the similarity structure of sample pairs, hence multiple kernel clustering based on graph learning (MKC-GL) achieves promising results on nonlinear clustering tasks. However, previous methods confine to a " ... ...

    Abstract Graph learning can effectively characterize the similarity structure of sample pairs, hence multiple kernel clustering based on graph learning (MKC-GL) achieves promising results on nonlinear clustering tasks. However, previous methods confine to a "three-stage" scheme, that is, affinity graph learning, Laplacian construction, and clustering indicator extracting, which results in the information distortion in the step alternating. Meanwhile, the energy of Laplacian reconstruction and the necessary cluster information cannot be preserved simultaneously. To address these problems, we propose a one-stage shifted Laplacian refining (OSLR) method for multiple kernel clustering (MKC), where using the "one-stage" scheme focuses on Laplacian learning rather than traditional graph learning. Concretely, our method treats each kernel matrix as an affinity graph rather than ordinary data and constructs its corresponding Laplacian matrix in advance. Compared to the traditional Laplacian methods, we transform each Laplacian to an approximately shifted Laplacian (ASL) for refining a consensus Laplacian. Then, we project the consensus Laplacian onto a Fantope space to ensure that reconstruction information and clustering information concentrate on larger eigenvalues. Theoretically, our OSLR reduces the memory complexity and computation complexity to O(n) and O(n
    Language English
    Publishing date 2023-04-04
    Publishing country United States
    Document type Journal Article
    ISSN 2162-2388
    ISSN (online) 2162-2388
    DOI 10.1109/TNNLS.2023.3262590
    Database MEDical Literature Analysis and Retrieval System OnLINE

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