Evaluating the dependability of reference-driven citation forecasts amid the COVID-19 pandemic: A bibliometric analysis across diverse journals.

Evaluating the dependability of reference-driven citation forecasts amid the COVID-19 pandemic: A bibliometric analysis across diverse journals.

Publication date: Jan 19, 2024

The journal impact factor significantly influences research publishing and funding decisions. With the surge in research due to COVID-19, this study investigates whether references remain reliable citation predictors during this period. Four multidisciplinary journals (PLoS One, Medicine [Baltimore], J. Formos. Med. Assoc. , and Eur. J. Med. Res. ) were analyzed using the Web of Science database for 2020 to 2022 publications. The study employed descriptive, predictive, and diagnostic analytics, with tools such as 4-quadrant radar plots, univariate regressions, and country-based collaborative maps via the follower-leading cluster algorithm. Six countries dominated the top 20 affiliations: China, Japan, South Korea, Taiwan, Germany, and Brazil. References remained strong citation indicators during the COVID-19 period, except for Eur. J. Med. Res. due to its smaller sample size (nā€…=ā€…492) than other counterparts (i. e., 41,181, 12,793, and 1464). Three journals showed higher network density coefficients, suggesting a potential foundation for reference-based citation predictions. Despite variations among journals, references effectively predict article citations during the COVID-19 era, underlining the importance of network density. Future studies should delve deeper into the correlation between network density and citation prediction.

Concepts Keywords
Bibliometric Baltimore
China Based
Korea Citation
Pandemic Covid
Density
Dependability
Eur
Evaluating
Journals
Med
Network
Period
Reference
References
Res

Semantics

Type Source Name
disease MESH COVID-19 pandemic
disease IDO country
disease IDO algorithm
disease MESH Long Covid

Original Article

(Visited 1 times, 1 visits today)