Knowledge domain and emerging trends in medication literacy research from 2003 to 2024: a scientometric and bibliometric analysis using CiteSpace and VOSviewer - Deng, Penghong; Liu, Xiaoxia; Li, Caiyun; Zhu, Xingping; Cui, Junli; Hua, Ping; Chen, Gang (Frontiers in Public Health, 2025)
BackgroundMedication 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.MethodsA 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.ResultsThe 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.ConclusionScientometric 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.