The Most Cited Mesh Terms and Authors who Published Papers in Pubmed Central on the Topic of Medicine and Health Using Bibliometric Analyses
Tsair-Wei Chien
Department of Research, Chi-Mei Medical Center, Tainan, Taiwan.
Hing-Man Wu
Department of Physical Medicine and Rehabilitation, Chi Mei Medical Center, Tainan, Taiwan.
Hsien-Yi Wang
Department of Nephrology, Chi-Mei Medical Center, Tainan, Taiwan.
Willy Chou *
Department of Physical Medicine and Rehabilitation, Chi Mei Medical Center, Tainan, Taiwan and Department of Recreation and Health-Care Management, Institute of Recreation Industry Management, Chia Nan University of Pharmacy, Tainan, Taiwan.
*Author to whom correspondence should be addressed.
Abstract
Aims: We visualized the current state of research on publication outputs and citations in the field of medicine and health to uncover topic burst and citations among medical subject headings (MeSH) clusters.
Study Design: A bibliometric analysis.
Place and duration of Study: Using Pubmed indexed articles to inspect the characteristics of topics on medicine and health since 1969.
Methodology: Selecting 156 abstracts, author names, countries, and MeSH terms on January 10, 2019, from Pubmed Central (PMC) based on the terms of medicine and health in the title since 1969, we applied the x-index and impact factor to evaluate author individual research achievements and compute MeSH bibliometric performances. The bootstrapping method was used to estimate the median and its 95% confidence intervals and make differences in metrics among MeSH clusters. The dominant nations were selected using the x-index to display on a dashboard. We programmed Microsoft Excel VBA routines to extract data. Google Maps and Pajek software were used for displaying graphical representations.
Results: We found that (1)the dominant countries/areas are the Unlited States, Taiwan, and Australia; (2) the author Grajales, Francisco Jose 3rd form Canada has the most cited metrics such as author IF=39.46 and x-index=6.28; (3)the MeSH terms of organization & administration, standards, and prevention & control gain the top three degree centralities among MeSH clusters; (4) No any differences in metrics were found among MeSH clusters; (5) the article(PMID= 24518354) with three MeSH term of delivery of health care, social media, and software and published in 2014 was cited most at least 62 times.
Conclusion: Social network analysis provides wide and deep insight into the relationships among MeSH terms. The MeSH weighted scheme and x-index were recommended to academics for computing MeSH citations in the future.
Keywords: X-inde, medical subject headings, Google Maps, social network analysis, medicine and health, bibliometric analysis