Volume 23 Issue 7
Jul.  2025
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Article Navigation >  Chinese Journal of General Practice  > 2025  >  23(7): 1085-1089
DONG Xueying, SHI Xiuxin, WANG Qiang, MA Dongping, CHEN Haiyu, MA Xiaolong, WANG Guowen. Quantitative analysis and evaluation of rural healthcare policies from the perspective of policy tools[J]. Chinese Journal of General Practice, 2025, 23(7): 1085-1089. doi: 10.16766/j.cnki.issn.1674-4152.004070
Citation: DONG Xueying, SHI Xiuxin, WANG Qiang, MA Dongping, CHEN Haiyu, MA Xiaolong, WANG Guowen. Quantitative analysis and evaluation of rural healthcare policies from the perspective of policy tools[J]. Chinese Journal of General Practice, 2025, 23(7): 1085-1089. doi: 10.16766/j.cnki.issn.1674-4152.004070
shu

Quantitative analysis and evaluation of rural healthcare policies from the perspective of policy tools

doi: 10.16766/j.cnki.issn.1674-4152.004070
  • 1.

    School of Medicine management, Shandong First Medical University, Taian, Shandong 271016, China

Funds:

 72004164

  • Received Date: 2025-01-14
    Available Online: 2025-10-25
    Abstract
  •   Objective   Rural healthcare is widely acknowledged to be a fundamental safeguard for the health of rural residents. In recent years, a plethora of relevant policies have been continuously introduced and adjusted. However, systematic analytical frameworks and effectiveness evaluations of these policies remain inadequate. The present study analyses and evaluates rural healthcare policies from the perspective of policy quantification research, with a view to providing references for the formulation and improvement of policies.   Methods   A three-dimensional analytical framework was constructed for the purpose of this study. The framework was based on national-level rural healthcare policies that were issued between 2009 and 2023. The three dimensions of the framework are as follows: "policy instrument", "policy process" and "policy intensity ". The policy modeling consistency (PMC) model was utilized to quantitatively evaluate seven key policies, with content analysis being applied to the relevant policy texts.   Results   A significant disparity was identified among the three policy instrument categories: supply-oriented (54.66%), demand-oriented (18.63%) and environment-oriented (26.71%). The policy process was divided into three phases: exploration (22.55%), advancement (22.30%) and deepening (55.15%). The mean policy intensity score was 3.73. The mean PMC index was 6.66, with policy instruments scoring highest (0.95) and policy timeliness scoring lowest (0.33).   Conclusion   There are structural imbalances in the in policy instruments. It is recommended that the use of demand- and environment-oriented instruments is increased and their internal composition optimized. As policy instruments emphasize different priorities at different stages, integrating them with process values and aligning them with stage-specific characteristics is crucial. Overall, policy intensity remains weak, necessitating a strengthening of top-level design and interdepartmental collaboration. Although all policies achieved PMC index ratings of "good" or higher, there is scope for further improvement. Recommendations include enriching policy content, improving multi-dimensional coordination and optimizing synergies.

     

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