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ORIGINAL RESEARCH article
Front. Public Health
Sec. Public Health Policy
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1500603
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AbstractBackground: The high-quality development of the traditional Chinese medicine (TCM) industry is dependent on supportive policies and requires higher levels of coordination and integration. National and local government policies must coordinate the integrated development of the TCM industry under modern governance principles. However, the policy structure and its impact on the development of the TCM industry have not been thoroughly explored from the perspectives of policy design and empirical evidence.Methods: A TCMIDPs evaluation system was established using the PMC-Index model to quantitatively analyze TCMIDPs in China and Shaanxi Province. Additionally, principal component analysis (PCA) was used to comprehensively evaluate 51 enterprises in Shaanxi Province. Based on industry development, this study constructs a reasonable evaluation index system for TCMIDPs using the PMC-Index model, analyzing the quality of TCM industry development policies. The PCA method was used to investigate the main problems in the TCM industry in Shaanxi Province by collecting data from 51 TCM preparation enterprises and proposing improvement paths.Results: The policy analysis shows that the average PMC index of the 31 TCM industry development policies is 5.080, indicating that the areas and functions covered by these policies need improvement. The comprehensive evaluation reveals that pharmaceutical enterprises in Shaanxi Province still exhibit unbalanced development.Conclusions: Future TCM industry policies should focus on long-term industrial development, incorporating comprehensive perspectives. Emphasis should be placed on strengthening enterprise R&D, accelerating pharmaceutical innovation, and promoting the holistic development of pharmaceutical enterprises.
Keywords: Traditional Chinese medicine policies, Traditional Chinese medicine (TCM) industry development, PMC-Index model, principal component analysis (PCA), Comprehensive evaluation
Received: 23 Sep 2024; Accepted: 10 Feb 2025.
Copyright: © 2025 Zhu, Lin, Liu, Liu, Yin and Feng. 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:
Bianling Feng, Xi'an Jiaotong University, Xi'an, 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.
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