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    Please use this identifier to cite or link to this item: https://ir.cnu.edu.tw/handle/310902800/32511

    標題: Whether article types of a scholarly journal are different in cited metrics using cluster analysis of MeSH terms to display A bibliometric analysis
    作者: Tsair-Wei Chien(錢才瑋)
    Hsien-Yi Wang(王憲奕)
    Wei-Chih Kan(甘偉志)
    Su, Shih-Bin
    貢獻者: Chia Nan Univ Pharm & Sci, Coll Leisure & Recreat Management
    Chia Nan Univ Pharm & Sci, Coll Leisure & Recreat Management
    Chia Nan Univ Pharm & Sci, Coll Leisure & Recreat Management, Dept Sport Management
    Chung Hwa Univ Med Technol, Dept Biol Sci & Technol, Tainan, Taiwan
    Southern Taiwan Univ Sci & Technol, Dept Leisure Recreat & Tourism Management, Tainan, Taiwan
    Chi Mei Med Ctr, Dept Occupat Med, Tainan, Taiwan
    Chi Mei Med Ctr, Dept Med Res, Tainan, Taiwan
    關鍵字: article type
    authorship-weighted scheme
    Google Maps
    medical subject headings
    PubMed central
    social network analysis
    日期: 2019-10
    上傳時間: 2020-07-29 13:47:59 (UTC+8)
    摘要: Background: 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 authorshipweighted scheme (AWS) to meet the prerequisites above and then demonstrate the applications for scholars. Methods: By searching the PubMed database (pubmed.com), we used the keyword "Medicine" [journal] and downloaded 5,636 articles published from2012 to 2016. A total number of 9,758 were cited in PubmedCentral (PMC). Ten MeSHterms 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. Results: We found that KendallWis 0.97 (x = 26.22, df= 9, P<.001) congruent with internal consistency on metrics across MeSH clusters. Both article types of methods and therapeutic use show higher frequencies than other 8 counterparts. The author Klaus Lechner (Austria) earns the highest research achievement(the mean of core articles on g= Ag= 15.35, AIF= 21, x= 3.92, h= 1) with one paper (PMID: 22732949, 2012), which was cited 23 times in 2017 and the preceding 5 years. Conclusion: Publishing article type with study methodology and design might lead to a higher IF. Both classifying article types and quantifying co-author contributions can be accommodated to other scientific disciplines. As such, which type of articles and who contributes most to a specific journal can be evaluated in the future.
    關聯: Medicine, v.98, n.43, e17631
    Appears in Collections:[醫務管理系(所)] 期刊論文
    [運動管理系] 期刊論文

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