The Hirsch-index in self-citation rates with articles in Medicine (Baltimore): Bibliometric analysis of publications in two stages from 2018 to 2021

Abstract

Background: The Hirsch-index (h-index) is a measure of academic productivity that incorporates both the quantity and quality of an author’s output. However, it is still affected by self-citation behaviors. This study aims to determine the research output and self-citation rates (SCRs) in the Journal of Medicine (Baltimore), establishing a benchmark for bibliometrics, in addition to identifying significant differences between stages from 2018 to 2021. Methods: We searched the PubMed database to obtain 17,912 articles published between 2018 and 2021 in Medicine (Baltimore). Two parts were carried out to conduct this study: the categories were clustered according to the medical subject headings (denoted by midical subject headings [MeSH] terms) using social network analysis; 3 visualizations were used (choropleth map, forest plot, and Sankey diagram) to identify dominant entities (e.g., years, countries, regions, institutes, authors, categories, and document types); 2-way analysis of variance (ANOVA) was performed to differentiate outputs between entities and stages, and the SCR with articles in Medicine (Baltimore) was examined. SCR, as well as the proportion of self-citation (SC) in the previous 2 years in comparison to SC were computed. Results: We found that South Korea, Sichuan (China), and Beijing (China) accounted for the majority of articles in Medicine (Baltimore); ten categories were clustered and led by 3 MeSh terms: methods, drug therapy, and complications; and more articles (52%) were in the recent stage (2020–2021); no significant difference in counts was observed between the 2 stages based on the top ten entities using the forest plot (Z = 0.05, P = .962) and 2-way ANOVA (F = 0.09, P = .76); the SCR was 5.69% (<15%); the h-index did not differ between the 2 collections of self-citation inclusion and exclusion; and the SC in the previous 2 years accounted for 70% of the self-citation exclusion. Conclusion: By visualizing the characteristics of a given journal, a breakthrough was made. Subject categories can be classified using MeSH terms. Future bibliographical studies are recommended to perform the 2-way ANOVA and then compare the outputs from 2 stages as well as the changes in h-indexes between 2 sets of self-citation inclusion and exclusion.

Using chord diagrams to explore article themes in 100 top-cited articles citing Hirsch’s h-index since 2005: A bibliometric analysis

“…[46] Citation cartels have thus been established in the research domain, where members of these cartels mutually cite papers of authors with whom they are familiar or unfamiliar. [45] It is possible to discover citation cartels using modern semantic web tools for manipulating the knowledge on the Internet. [46] However, it is difficult to determine whether this citation cartel holds true in real life.…”

“…Journal editors and reviewers are well aware of this phenomenon of citation cartel. In a previous study, [45] a module was developed for computing the self-citation rate for specific journals below or above the cutting point of 20%, [47] 15%, [48] or 10%. [49] 1.…”

“…[46] Citation cartels have thus been established in the research domain, where members of these cartels mutually cite papers of authors with whom they are familiar or unfamiliar. [45]…”

A leading author of meta-analysis does not have a dominant contribution to research based on the CJAL score: Bibliometric analysis

“…[ 26 ] One way editors can increase their journal’s impact factor (IF) is through inter-journal citations. [ 27 ] More recently, citation cartels have also been observed between editors and authors. [ 28 ] These cartels involve members citing each other’s papers for mutual gain, regardless of familiarity.…”

“…[ 28 ] These cartels involve members citing each other’s papers for mutual gain, regardless of familiarity. [ 27 ]…”