Whether article types of a scholarly journal are different in cited metrics using cluster analysis of MeSH terms to display: A bibliometric analysis

• Published in: Medicine (Baltimore) 2019-10, Vol.98 (43), p.e17631-e17631
• Main Authors: Chien, Tsair-Wei, Wang, Hsien-Yi, Kan, Wei-Chih, Su, Shih-Bin
• Format: Article
• Language: English
• Subjects: Authorship; Bibliometrics; Cluster Analysis; Humans; Medical Subject Headings; Periodicals as Topic; Publishing; PubMed; Systematic Review and Meta-Analysis
• ISSN: 0025-7974; 1536-5964
• DOI: 10.1097/MD.0000000000017631

Many authors are concerned which types of peer-review articles can be cited most in academics and who were the highest-cited authors in a scientific discipline. The prerequisites are determined by: (1) classifying article types; and (2) quantifying co-author contributions. We aimed to apply Medical Subject Headings (MeSH) with social network analysis (SNA) and an authorship-weighted scheme (AWS) to meet the prerequisites above and then demonstrate the applications for scholars. By searching the PubMed database (pubmed.com), we used the keyword "Medicine" [journal] and downloaded 5,636 articles published from 2012 to 2016. A total number of 9,758 were cited in Pubmed Central (PMC). Ten MeSH terms were separated to represent the journal types of clusters using SNA to compare the difference in bibliometric indices, that is, h, g, and x as well as author impact factor(AIF). The methods of Kendall coefficient of concordance (W) and one-way ANOVA were performed to verify the internal consistency of indices and the difference across MeSH clusters. Visual representations with dashboards were shown on Google Maps. We found that Kendall W is 0.97 (χ = 26.22, df = 9, P