Car Josip, Carlstedt-Duke Jan, Tudor Car Lorainne, Posadzki Pawel, Whiting Penny, Zary Nabil, Atun Rifat, Majeed Azeem, Campbell James
Centre for Population Health Sciences, Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, Singapore.
Nanyang Institute of Technology in Health and Medicine, Nanyang Technological University Singapore, Singapore, Singapore.
J Med Internet Res. 2019 Feb 14;21(2):e12913. doi: 10.2196/12913.
Synthesizing evidence from randomized controlled trials of digital health education poses some challenges. These include a lack of clear categorization of digital health education in the literature; constantly evolving concepts, pedagogies, or theories; and a multitude of methods, features, technologies, or delivery settings. The Digital Health Education Collaboration was established to evaluate the evidence on digital education in health professions; inform policymakers, educators, and students; and ultimately, change the way in which these professionals learn and are taught. The aim of this paper is to present the overarching methodology that we use to synthesize evidence across our digital health education reviews and to discuss challenges related to the process. For our research, we followed Cochrane recommendations for the conduct of systematic reviews; all reviews are reported according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidance. This included assembling experts in various digital health education fields; identifying gaps in the evidence base; formulating focused research questions, aims, and outcome measures; choosing appropriate search terms and databases; defining inclusion and exclusion criteria; running the searches jointly with librarians and information specialists; managing abstracts; retrieving full-text versions of papers; extracting and storing large datasets, critically appraising the quality of studies; analyzing data; discussing findings; drawing meaningful conclusions; and drafting research papers. The approach used for synthesizing evidence from digital health education trials is commonly regarded as the most rigorous benchmark for conducting systematic reviews. Although we acknowledge the presence of certain biases ingrained in the process, we have clearly highlighted and minimized those biases by strictly adhering to scientific rigor, methodological integrity, and standard operating procedures. This paper will be a valuable asset for researchers and methodologists undertaking systematic reviews in digital health education.
综合数字健康教育随机对照试验的证据存在一些挑战。这些挑战包括文献中数字健康教育缺乏明确的分类;概念、教学法或理论不断演变;以及众多的方法、特征、技术或交付环境。数字健康教育合作组织的成立是为了评估卫生专业数字教育的证据;为政策制定者、教育工作者和学生提供信息;并最终改变这些专业人员学习和被教导的方式。本文的目的是介绍我们在数字健康教育综述中用于综合证据的总体方法,并讨论与该过程相关的挑战。对于我们的研究,我们遵循了Cochrane关于进行系统综述的建议;所有综述均按照PRISMA(系统综述和Meta分析的首选报告项目)指南进行报告。这包括召集数字健康教育各个领域的专家;确定证据基础中的差距;制定有针对性的研究问题、目标和结果指标;选择合适的搜索词和数据库;定义纳入和排除标准;与图书馆员和信息专家联合进行搜索;管理摘要;检索论文的全文版本;提取和存储大型数据集,严格评估研究质量;分析数据;讨论研究结果;得出有意义的结论;以及撰写研究论文。从数字健康教育试验中综合证据所采用的方法通常被视为进行系统综述最严格的基准。尽管我们承认该过程中存在某些固有偏差,但我们通过严格坚持科学严谨性、方法完整性和标准操作程序,明确突出并尽量减少了这些偏差。本文对于在数字健康教育领域进行系统综述的研究人员和方法学家将是一份宝贵的资料。
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