USING SOCIAL NETWORK ANALYSIS TO UNDERSTAND A HEALTH-RELATED JOURNAL

Chi-Sheng Chang

1.Center for Quality Management, Chi Mei Medical Center, Liouying,Tainan,Taiwan.

2.Department of Industrial Engineering and Management, National Yunlin University of Science and Technology, Yunlin, Taiwan

This email address is being protected from spambots. You need JavaScript enabled to view it.

Cheng-Fang Hung

Department of Industrial Engineering and Management, National Yunlin University of Science and Technology, Yunlin, Taiwan

rasch.smile@gmail com

Tung-Hui Jen *

Department of Chinese Medicine, Chi-Mei Medical Center, Taiwan

*Corresponding Author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

Our aims were to investigate journal features by collecting data from Medline and to visualize the journal characteristicsfor Eur J Cancer Care (Engl) (EJCC). Selecting 1611 abstracts and their corresponding coauthor names and keywords on September 3, 2017 from the Medline, we analyzed author names and keywords to report fol-

lowing features: (1) nation distribution and coauthor collaboration; (2) journal features represented by paper keywords; (3) the most productive authors and their authorship clusters; (4) the top 10 journals most similar to EJCC. We programmed Microsoft Excel VBA routines to extract data from Medline. Rasch model and SNA Pajek software were performed to display visualized representations for EJCC features. We found that (1) the most number of papers are from nations of UK (28%) Australia (10%), and Sweden (5%); (2) The most linked Keywords are cancer and breast cancer; (3) the most productive author is R Sanson-Fisher; (4) The top one ranked journals most similar to EJCC is Support Care Cancer.. Social network analysis provides wide and deep insight into the relationships among nations, coauthor collaborations, abstract keywords, and journals most similar to EJCC. The results can be offered to strategy and decision making for the target journal. 

Keywords: abstract keywords, authorship collaboration, Rasch analysis, social network analysis, Medline

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