已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

Knowledge domain and emerging trends in medication literacy research from 2003 to 2024: a scientometric and bibliometric analysis using CiteSpace and VOSviewer

多学科方法 科学计量学 文献计量学 科学网 健康素养 趋势分析 引用 图书馆学 医学教育 心理学 医学 医疗保健 政治学 梅德林 计算机科学 社会科学 社会学 法学 机器学习
作者
Penghong Deng,Xiaoxia Liu,Caiyun Li,Xingping Zhu,Jian Cui,Ping Hua,Gang Chen
出处
期刊:Frontiers in Public Health [Frontiers Media]
卷期号:13
标识
DOI:10.3389/fpubh.2025.1598482
摘要

Medication literacy (ML) has emerged as a critical global public health concern, garnering growing scholarly attention over the past two decades. To delineate major research domains, identify evolving trends, and inform future research priorities, we conducted a scientometric analysis of the scientific literature on ML. A systematic search was performed to retrieve publications on ML from the Web of Science Core Collection, covering the period from 2003 to 2024. Scientometric analyses were executed using CiteSpace and VOSviewer to visualize and evaluate collaborative networks, including co-citation references, co-occurring keywords, and contributions by countries, institutions, authors, and journals. The analysis incorporated 1,968 eligible publications. A rapidly growing trend in research interest in ML was observed, with an average annual growth rate of 46.1% in publications between 2003 and 2022. Three major research trends were identified: relationship between ML and medication adherence, the development of ML-specific assessment tools, and investigation of psychosocial factors associated with ML. The United States of America, Northwestern University, Davis Tc, and Patient Education and Counseling were identified as the most cited and influential entities within this field, representing the leading country, institution, author, and journal, respectively. Scientometric analysis provides invaluable insights to clinicians and researchers involved in ML research by identifying leading contributors, intellectual bases and research trends. ML is evolving from unidimensional analysis to multidisciplinary exploration of dynamic mechanisms. Future research on ML is facing significant challenges, including the exploration of adherence mechanisms, validation of digital assessment tools, and the moderating effect model of socio-psychological factors on ML.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
空间完成签到 ,获得积分10
2秒前
5秒前
molihuakai应助能干的土豆采纳,获得10
6秒前
6秒前
Ruby完成签到,获得积分10
6秒前
7秒前
7秒前
GYJ完成签到,获得积分10
8秒前
ixueyi完成签到,获得积分10
8秒前
笑点低秋柳完成签到 ,获得积分10
9秒前
乔木木完成签到,获得积分10
9秒前
坚强的睿渊完成签到 ,获得积分10
9秒前
wpz发布了新的文献求助10
10秒前
gstaihn完成签到,获得积分10
12秒前
虚心的紫夏完成签到,获得积分10
13秒前
zoeky发布了新的文献求助10
13秒前
愿不负丶发布了新的文献求助10
13秒前
12321234完成签到,获得积分10
16秒前
愿不负丶完成签到,获得积分10
19秒前
思柔完成签到 ,获得积分10
22秒前
Jepsen完成签到 ,获得积分10
25秒前
29秒前
30秒前
深情的从丹完成签到,获得积分10
33秒前
好好好发布了新的文献求助10
36秒前
didiaonn完成签到,获得积分10
36秒前
两回事完成签到 ,获得积分10
36秒前
咕咕呱呱完成签到 ,获得积分10
40秒前
muliushang完成签到 ,获得积分10
40秒前
41秒前
42秒前
Ronalsen完成签到 ,获得积分10
43秒前
耍酷的鹰完成签到,获得积分10
44秒前
若月画萤完成签到,获得积分10
45秒前
46秒前
xu完成签到,获得积分10
46秒前
XPDHW发布了新的文献求助10
46秒前
晴风发布了新的文献求助10
47秒前
49秒前
侯侯完成签到,获得积分10
50秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
China Pluperfect I: Epistemology of Past and Outside in Chinese Art 520
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
基于锂离子电池正极材料回收的绿色溶剂开发及工程化应用研究 500
Auslegungsgeschichte 500
Cosmos as Art Object: Studies in Plato's Timaeus and Other Dialogues 500
Middle East Patterns 444
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7639471
求助须知:如何正确求助?哪些是违规求助? 9212740
关于积分的说明 19762710
捐赠科研通 7206115
什么是DOI,文献DOI怎么找? 3276031
关于科研通互助平台的介绍 2437585
邀请新用户注册赠送积分活动 2273310