Article

저자키워드의 MeSH 부여 방법에 의한 우리나라 치의학 저널의 연구동향 분석

정소나1,2, 정지나3,*
Sona Jeong1,2, Ji Na Jeong3,*
Author Information & Copyright
1가톨릭대학교 성의교정 도서관
2숙명여자대학교 문헌정보학과
3전주대학교 보건관리학과
1Medical Library, The Catholic University of Korea, Seoul
2Department of Library & Information Science, Sookmyung Women’s University, Seoul
3Department of Health Management, Jeonju University, Jeonju, Korea
*Corresponding author : Ji Na Jeong, Department of Health Management, Jeonju University, 303 Wansan-gu Cheonjam-ro, Jeonju 55069, Korea, Tel: 82-63-220-2506, Fax: 82-63-220-2054, E-mail: naji2004@JJ.ac.kr

ⓒ Copyright 2020 The Korean Medical Library Association. This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Received: Oct 15, 2020; Revised: Nov 20, 2020; Accepted: Dec 16, 2020

Published Online: Dec 30, 2020

Abstract

Researchers seek to identify optimal journals for submission based on their studies but tend to rely on journal impact factors or scientific journal rankings. We investigated research trends by selecting highfrequency words from author keywords (AKs), analyzing subject areas, and performing quantitative data analysis of Korean dental journals. Consequently, we suggest a method for choosing journals that fit a specific subject area. We used a corpus of 9 Korean dentistry journals regarded in Korea as quality internationally approved journals. AKs occurring more than 10 times were assigned to Medical Subject Headings (MeSH) terms and subcategories, which were then categorized using the MeSH tree structure. Knowledge- Matrix Plus and VOSviewer were used to analyze network relationships, density, and clustering. The AKs were of 7527 types, 15,960 terms, and formed 54 clusters. The AKs with 10+ occurrence were 199 types, 4289 terms, and formed 9 clusters. Assigning the AKs with 10+ occurrence to MeSH terms led to expanding 732 types of AK terms into 249 types with 9 clusters and 4268 links. Core study areas over the past 10 years were facial asymmetry, a topic under oral surgery and medicine, and orthognathic surgery focused on mandibular fractures, followed by shear bond strength of zirconia. Analyzing 16 MeSH subject categories, we found that the “analytical, diagnostic and therapeutic techniques and equipment” category had the largest distribution of AKs (40.7%). This was followed by “diseases” (21.2%) and “anatomy” (14.90%). The orthognathic surgery cluster was the largest, followed by the shear bond strength cluster. Dental implants is a core area with strong links to highoccurrence words, such as cone-beam computed tomography and mandible, which were distributed in the order of The Journal of Advanced Prosthodontics (37.8%) and Journal of Periodontal & Implant Science (30.6%). Five clusters were closely packed in the center, 2 clusters were formed above the center, 1 cluster was formed below the center, and a cluster on the right was widespread. Cluster analysis using AKs and MeSH may be a good analytic method for researchers to determine expanding research areas and select optimal journals for paper submission.

Keywords: Co-Word Analysis; Dentistry; Medical Subject Headings; Network Analysis; Clustering; Subject Categories