Lee Jason Wen Yau, Tan Jenelle Yingni, Bello Fernando
Duke-NUS Medical School, National University of Singapore, 8 College Road, Singapore, 169857, Singapore, 65 66016437.
Faculty of Arts and Social Science, National University of Singapore, Singapore, Singapore.
JMIR Med Educ. 2025 Jul 16;11:e67873. doi: 10.2196/67873.
With the growing use of technology in medical education, a framework is needed to evaluate learners' and educators' acceptance of these technologies. In this context, the Technology Acceptance Model (TAM) offers a valuable theoretical framework, providing insights into the determinants influencing users' acceptance and adoption of technology.
This review aims to systematically synthesize the body of research in medical education that uses the TAM.
An electronic literature search was conducted using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach in February 2024 on the Embase, MEDLINE, PsycINFO, PubMed, and Web of Science databases, yielding 680 articles. Upon elimination of duplicates and applying the exclusion criteria, a total of 39 articles were retained. To evaluate the quality of the study, the Medical Education Research Study Quality Instrument score was calculated for each analysis with a qualitative component.
Studies using TAM in medical education began in 2010, with the model's application relatively rare up to 2016. Most of the studies were quantitative, operationalizing the TAM as a survey instrument, but it was also used as a research framework in qualitative data analysis. Structural equation modeling, descriptive analysis, and correlation analysis were the most common data analysis approaches in the studies. E-learning and mobile learning were the predominant learning interventions explored, but there were indications that novel learning technologies such as augmented reality, virtual reality, and 3D printing were being investigated.
The study's findings reveal an expanding scholarly engagement with using TAM in medical education. Although the TAM has been mostly used as a survey instrument, it can also be adapted as a qualitative research framework to analyze data. This systematic review provides a foundation for future research to understand the factors influencing users' acceptance of technology, especially in medical education.
随着技术在医学教育中的应用日益广泛，需要一个框架来评估学习者和教育工作者对这些技术的接受程度。在此背景下，技术接受模型（TAM）提供了一个有价值的理论框架，有助于深入了解影响用户接受和采用技术的决定因素。
本综述旨在系统地综合医学教育中使用TAM的研究成果。
2024年2月采用PRISMA（系统评价和Meta分析的首选报告项目）方法，在Embase、MEDLINE、PsycINFO、PubMed和Web of Science数据库中进行电子文献检索，共获得680篇文章。在消除重复项并应用排除标准后，共保留39篇文章。为评估研究质量，对每项包含定性成分的分析计算医学教育研究质量工具得分。
医学教育中使用TAM的研究始于2010年，在2016年之前该模型的应用相对较少。大多数研究是定量研究，将TAM作为一种调查工具来实施，但它也被用作定性数据分析的研究框架。结构方程建模、描述性分析和相关性分析是这些研究中最常见的数据分析方法。电子学习和移动学习是所探讨的主要学习干预方式，但有迹象表明，诸如增强现实、虚拟现实和3D打印等新型学习技术也在被研究。
研究结果表明，在医学教育中使用TAM的学术参与度在不断提高。尽管TAM主要被用作一种调查工具，但它也可以被改编为一个定性研究框架来分析数据。本系统综述为未来研究理解影响用户接受技术的因素提供了基础，尤其是在医学教育领域。
