摘要
目的探讨中英文检索数据库中关于灾害护理文献的主题内容、热度变化,为相关研究提供参考,为促进灾害护理领域的健康发展提供理论依据与情报支撑。方法利用潜在狄利克雷分配(LDA)主题模型对中国知网、万方、Web of Science等中英文数据库中检索截止2021年9月6日相关灾害护理的文献主题进行建模,生成"主题-词"分布概率矩阵并分析结果。结果LDA模型分析显示,中文文献有13个研究主题,并可归为4个研究方向,包括灾害伤病员的护理及救治、国内外灾害护理现状及院前急救发展、突发公共卫生应急事件救护能力评估及保障、灾害护理人员能力培训及考核;英文文献有6个研究主题。结论应用LDA模型文本提取结果可准确地挖掘出灾害护理领域的研究主题,有利于了解灾害护理的发展动态和研究热点,能够为相关研究提供可靠的参考依据。
Objective To explore the subject content and hot changes of disaster nursing in the Chinese and English databases,so as to provide references for related research,and to provide theoretical basis and information support for promoting the healthy development of disaster nursing.Methods The Latent Dirichlet Allocation(LDA)model was used to model the literature topics on disaster nursing in Chinese and English databases such as China National Knowledge Infrastructure(CNKI),WanFang,and Web of Science until September 6,2021,and generate a"topic-word"distribution probability matrix and analyze the results.Results The analysis of the LDA model showed that there were 13 research topics in the Chinese literature,which could be grouped into 4 research directions,including the nursing and treatment of disaster victims,status of disaster nursing at home and abroad and development of pre-hospital first aid,assessment and guarantee of emergency rescue capacity in public health emergencies,capacity training and assessment of disaster nurses.The English literature had 6 research themes.Conclusions Based on the extraction results of the LDA model,the research topics in the field of disaster nursing are accurately excavated,which is conducive to understanding the development trends and research hotspots of disaster nursing,and can provide a reliable reference for related research.
作者
王惠峰
尼码旺堆
段红梅
Wang Huifeng;Nima Wangdui;Duan Hongmei(School of Nursing,Beijing University of Chinese Medicine,Beijing 102488,China)
出处
《中华现代护理杂志》
2022年第16期2116-2121,共6页
Chinese Journal of Modern Nursing
关键词
文献计量学
灾害护理
LDA模型
主题挖掘
Bibliometrics
Disaster nursing
Latent Dirichlet Allocation model
Topic mining