Jin Jian, Du Hongbin, Liu Zhaoyu
School of Economics, Hebei University, Baoding, China.
Department of Finance, Affiliated Hospital of Hebei University, Baoding, China.
PLoS One. 2025 Aug 7;20(8):e0329300. doi: 10.1371/journal.pone.0329300. eCollection 2025.
This paper aims to measure the government's attention to the health industry accurately, which is crucial for understanding policy directions and resource allocation strategies.
Addressing the limitations of traditional word frequency methods, such as restricted word segmentation and ambiguous terms, the BERTopic (Bidirectional Encoder Representations from Transformers Topic Modeling) is applied to measure government attention at the sentence level. Rule matching in an ambiguous dictionary, which is expanded by utilizing the word2vec model, is to achieve accurate identification of unclassified topics. This approach reveals policy concerns at the semantic level.
The BERTopic model is a more precise instrument for evaluating the health industry's attention. Furthermore, significant regional differences are detected. Attention in the Northeast has declined, remained stable in the Central and Western regions, and continuously increased in the Eastern region. The main areas of government attention in the health industry are sports and fitness, environmental governance, medical services, and healthy older individuals' care. The mode of medical care in healthy retirement has evolved to a combination of medical care and health preservation.
It is recommended that the balanced development of the health industry across regions be promoted based on specific local conditions. Efforts should also be made to enhance the efficiency of medical services, optimize the allocation mechanism for medical resources, establish a systematic medical treatment plan, and encourage the comprehensive and coordinated development of the health industry.
本文旨在准确衡量政府对健康产业的关注程度，这对于理解政策方向和资源分配策略至关重要。
针对传统词频方法的局限性，如分词受限和术语模糊等问题，应用BERTopic（基于变换器的双向编码器表示主题建模）在句子层面衡量政府关注度。利用word2vec模型扩展的模糊词典进行规则匹配，以实现对未分类主题的准确识别。这种方法在语义层面揭示了政策关注点。
BERTopic模型是评估对健康产业关注度的更精确工具。此外，还发现了显著的区域差异。东北地区的关注度下降，中西部地区保持稳定，东部地区持续上升。政府在健康产业中的主要关注领域是体育健身、环境治理、医疗服务和老年健康照护。健康养老的医疗模式已演变为医疗与养生相结合。
建议根据各地具体情况促进健康产业的区域均衡发展。还应努力提高医疗服务效率，优化医疗资源配置机制，制定系统的治疗方案，并鼓励健康产业的全面协调发展。
