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中国全科医学 ›› 2026, Vol. 29 ›› Issue (34): 5033-5044.DOI: 10.12114/j.issn.1007-9572.2025.0574

• 热点研究 • 上一篇    

中国全科医学研究热点、前沿与趋势分析（2019—2024）：基于文献计量学、命名实体识别与主题模型的整合分析

王宏1, 杨信2, 牛晓翠1, 田朝伟1, 邓彬1, 甘幸雯1, 赖思伶1, 谢东霖1, 胡丙杰1,2,3,*()   

  1. 1．510000　广东省广州市，广州医科大学附属第二医院全科医学科
    2．510120　广东省广州市，广州医科大学附属第一医院全科医学科
    3．511436　广东省广州市，广州医科大学广东省高校基于大数据利用的卫生健康治理哲学社会科学重点实验室
  • 收稿日期:2026-04-23 修回日期:2026-06-09 出版日期:2026-12-05 发布日期:2026-10-26
  • 通讯作者: 胡丙杰


  • 作者贡献：

    胡丙杰负责研究构思与设计、质量控制及论文审校，对文章整体负责，监督管理；杨信负责研究方案讨论与结果解释；王宏、牛晓翠、邓彬负责数据收集与整理、资料分析、图表绘制及主题建模结果的初步解释；田朝伟负责提供临床专业意见，参与论文修订；牛晓翠、甘幸雯、赖思伶、谢东霖负责文献检索、数据清洗，参与主题关键词重要性分析与代表性文献人工审核；王宏负责论文撰写与修订。

  • 基金资助:
    广东省教育厅继续教育质量提升工程项目(JXJYGC2021DY0231); 广东省教育厅研究生教育创新计划项目(2021JGXM090，2023JGXM_111); 广州市教育科学规划课题(教学成果培育项目：2024111312)

Research Hotspots, Frontiers and Trends of General Practice in China (2019-2024): an Integrated Analysis Based on Bibliometrics, Named Entity Recognition and Topic Modeling

WANG Hong1, YANG Xin2, NIU Xiaocui1, TIAN Chaowei1, DENG Bin1, GAN Xingwen1, LAI Siling1, XIE Donglin1, HU Bingjie1,2,3,*()   

  1. 1. Department of General Practice, the Second Affiliated Hospital of Guangzhou Medical University, Guangzhou 510000, China
    2. Department of General Practice, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China
    3. Philosophy and Social Sciences Key Laboratory of Guangdong Higher Education Institutes for Health Governance Based on Big Data Utilization, Guangzhou Medical University, Guangzhou 511436, China
  • Received:2026-04-23 Revised:2026-06-09 Published:2026-12-05 Online:2026-10-26
  • Contact: HU Bingjie

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摘要： 背景 我国全科医学研究快速发展，但国内核心期刊相关文献的结构与演变缺乏系统量化评估。 目的 利用文献计量学、命名实体识别（NER）与基于双向编码器表示模型（BERT）的主题建模方法（BERTopic）进行主题建模，刻画2019—2024年核心期刊中国全科医学文献的产出特征、区域差异和主题结构。 方法 数据源自中国生物医学文献服务系统（SinoMed），检索日期为2025-04-15，以中文医学主题词（CMeSH）检索SinoMed收录的中文核心期刊，纳入2019—2024年中国全科医学相关文献摘要共5 836篇。开展年度产出、发表期刊与作者机构分布、合作网络等计量学分析；采用医学领域NER提取疾病、症状、药物等8类实体，并在省市层面进行分布与特征分析；基于中文医学预训练嵌入BERTopic的主题模型。 结果 发文量由2019年1 213篇降至2024年789篇；基金资助率为69.4%，前10种期刊占36.0%，北京市和上海市合计占35.2%。BERTopic识别出31个主题并归为4类，其中方法学规范化、医防融合、人才培养主题持续上升，慢性病治疗与家庭医生签约趋稳或下降，新型冠状病毒感染主题呈阶段性波动。 结论 2019—2024年检索范围内的我国全科医学研究总体产出下降，区域分布高度集中，主题呈多元化，方法学应用规范化与医防融合主题关注度上升。建议优化资源配置与跨区域协作，推进多学科融合与方法学创新，加强人才培养，完善研究评价与资助机制，促进学科在应用转化与理论深化两个维度均衡发展。

关键词: 全科医学, 文献计量学, 命名实体识别, BERTopic, 预训练语言模型文本挖掘

Abstract:

Background

General practice in China has grown rapidly, yet the structure and evolution of articles published in Chinese core journals in this field have not been systematically quantified.

Objective

To characterize the output, regional differences, and thematic structure of general practice articles published in Chinese core journals from 2019 to 2024 by integrating bibliometrics, named entity recognition (NER), and BERTopic topic modeling.

Methods

SinoMed (China Biomedical Literature Database) was searched on April 15, 2025 using Chinese Medical Subject Headings (CMeSH), and 5 836 abstracts of general practice-related articles published in Chinese core journals from 2019 to 2024 were included. Bibliometric analyses covered annual output; the distribution of journals, authors, and institutions; and collaboration networks. A medical-domain NER model extracted eight categories of entities (e.g., diseases, symptoms, and drugs), whose distribution and characteristics were then analyzed at the provincial and municipal levels. A BERTopic model was subsequently built on embeddings from a Chinese medical pre-trained language model.

Results

Annual publication output decreased from 1 213 articles in 2019 to 789 in 2024; 69.4% of the articles reported funding support, the top 10 journals accounted for 36.0% of all publications, and Beijing and Shanghai together contributed 35.2%. Topic modeling identified 31 topics, grouped into four clusters. Topics on methodological standardization, the integration of medical care and prevention, and workforce development rose steadily, whereas chronic disease treatment and contracted family doctor services stabilized or declined, and COVID-19-related topics showed stage-specific fluctuations.

Conclusion

Within the search scope, general practice research in China from 2019 to 2024 showed an overall decline in output, pronounced regional concentration, and increasingly diversified themes, with growing attention to methodological standardization and the integration of medical care and prevention. These findings suggest the need to optimize resource allocation and cross-regional collaboration, advance multidisciplinary integration and methodological innovation, strengthen workforce development, and refine research evaluation and funding mechanisms, thereby promoting balanced development of the discipline in both practical translation and theoretical advancement.

Key words: General practice, Bibliometrics, Named entity recognition, BERTopic, Pre-trained language model text mining

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