Quantitative Evaluation of Elderly Health Policies in China Based on PMC Index Model

Corresponding author: yangfeilong, 19777039@qq.com

DOI: 10.12201/bmr.202406.00004

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  Abstract: Purpose/Significance: This study aims to discuss the construction of the current elderly health policy system through the results of policy text mining and quantitative evaluation, providing references for future policy adjustments and optimization. Method/Process: Elderly health-related policies issued by relevant departments of the State Council from 2019 to 2023 were selected as the research focus. The ROST text data mining tool was utilized to extract high-frequency word lists and construct semantic networks. Gephi was employed to visualize social network relationships, facilitating text analysis. Quantitative analysis of elderly health-related policies was performed using the PMC index model. Results/Conclusion: The research revealed that high-frequency words such as national, society, knowledge, resources, and guarantee are closely interconnected in the social network, indicating key areas of elderly health policies. The mean PMC index of policies reached 6.65, reflecting an overall positive trend in policy formulation. Furthermore, the study proposed relevant recommendations for policy optimization.

  Key words: Elderly health; Policy evaluation; Text Mining；PMC index model

  Submit time: 7 June 2024

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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