SeyyedHosseini Shohreh, Yazdankhahfard Mohammadreza, Azargoon Maryam, BasirianJahromi Reza
Deapartment of Medical Library and Information Science, School of Paramedicine, Bushehr University of Medical Sciences, Bushehr, Iran.
Department of Nursing, School of Nursing and Midwifery, Bushehr University of Medical Sciences, Bushehr, Iran.
Iran J Public Health. 2024 Sep;53(9):2121-2129. doi: 10.18502/ijph.v53i9.16465.
Nowadays, blended learning in medicine (BLM) has gained the attention of most experts as an invaluable approach to improving the quality of medical education. The level of attention to articles in this field on social networks is substantial. This study aimed to study the effectiveness of published articles in blended learning, indexed in Scopus and Web of Science databases between 2013 and 2022, from an altmetrics perspective.
The research is descriptive-analytical, with a scientometrics approach (using the Altmetrics index). The population includes all the articles on blended learning in medicine, indexed in Scopus and Web of Science databases, two well-known citation databases worldwide. Data were extracted using the Altmetrics bookmarklet tool and analyzed with descriptive statistics methods in Excel software.
Out of 1327 articles, 136 articles (10.25%) did not have a digital object identifier (DOI) or PMID number. Mendeley, X (previously Twitter), and Dimensions were the most widely used social networks in blended learning. The United States, the United Kingdom, and Australia had the highest number of tweets in blended learning in medicine.
The number of articles with altmetrics indicators, categorized by publication year, demonstrates an improvement in the familiarity and use of social media by blended learning researchers in medicine. Blended learning researchers are advised to carefully select reputable journals - preferably with DOI - to increase the visibility and attention to their articles on social media.
如今,医学混合式学习(BLM)作为提高医学教育质量的一种宝贵方法,已引起大多数专家的关注。该领域文章在社交网络上的受关注程度颇高。本研究旨在从替代计量学角度,研究2013年至2022年期间在Scopus和Web of Science数据库中索引的混合式学习已发表文章的有效性。
本研究为描述性分析,采用科学计量学方法(使用替代计量学指标)。研究总体包括在Scopus和Web of Science数据库(全球两个知名的引文数据库)中索引的所有医学混合式学习文章。数据使用替代计量学书签工具提取,并在Excel软件中用描述性统计方法进行分析。
在1327篇文章中,136篇文章(10.25%)没有数字对象标识符(DOI)或PubMed ID号。Mendeley、X(前身为Twitter)和Dimensions是混合式学习中使用最广泛的社交网络。美国、英国和澳大利亚在医学混合式学习方面的推文数量最多。
按出版年份分类的具有替代计量学指标的文章数量表明,医学混合式学习研究人员对社交媒体的熟悉程度和使用情况有所改善。建议混合式学习研究人员谨慎选择声誉良好的期刊——最好带有DOI——以提高其文章在社交媒体上的可见性和关注度。