Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Towards Actualizing the Value Potential of Korea Health Insurance Review and Assessment (HIRA) Data as a Resource for Health Research: Strengths, Limitations, Applications, and Strategies for Optimal Use of HIRA Data
2017512 citationsSeok‐Jun Yoon et al.Journal of Korean Medical Scienceprofile →
Author Peers
Peers are selected by citation overlap in the author's most active subfields.
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This map shows the geographic impact of Seok‐Jun Yoon's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Seok‐Jun Yoon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Seok‐Jun Yoon more than expected).
This network shows the impact of papers produced by Seok‐Jun Yoon. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Seok‐Jun Yoon. The network helps show where Seok‐Jun Yoon may publish in the future.
Co-authorship network of co-authors of Seok‐Jun Yoon
This figure shows the co-authorship network connecting the top 25 collaborators of Seok‐Jun Yoon.
A scholar is included among the top collaborators of Seok‐Jun Yoon based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Seok‐Jun Yoon. Seok‐Jun Yoon is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Choi, Won‐Ho, et al.. (2009). The Prediction of Health care Outcome of Total Hip Replacement Arthroplasty Patients using Charlson Comorbidity Index. 14(1). 23–35.2 indexed citations
13.
Lee, Juneyoung, et al.. (2008). A study on Factors Related with a Periodic General Health Examination. The Korean Data Analysis Society. 10(1). 119–131.2 indexed citations
14.
Lee, Juneyoung, et al.. (2005). An Experimental Design for Summary Measures of Population Health(SMPH) Related to a Quality of Life - An Application of Generalizability Theory -. The Korean Data Analysis Society. 7(6). 1971–1984.
15.
Lee, Jung‐Kyu, Seok‐Jun Yoon, Young Kyung, et al.. (2003). Disability Weights for Diseases in Korea. Journal of Preventive Medicine and Public Health. 36(2). 163–170.11 indexed citations
16.
Lee, Jung‐Kyu, et al.. (2002). Study of Disability-Adjusted Life Expectancy(DALE) Using National Health Interview Survey in Korea. Journal of Preventive Medicine and Public Health. 35(4). 331–339.1 indexed citations
17.
Yoon, Seok‐Jun, Chang‐Yup Kim, Young-Soo Shin, & Yong-Jun Choi. (2001). Measuring the Burden of Major Cancers in Korea Using Healthy Life-Year (HeaLY). Journal of Preventive Medicine and Public Health. 34(4). 372–378.1 indexed citations
18.
Yoo, Sun Mi & Seok‐Jun Yoon. (2001). Measuring the status of obesity prevalence and food habit of children in Asan city.. Gajeong yihag hoeji. 22(1). 78–86.2 indexed citations
19.
Kim, Chang‐Yup, et al.. (2000). Measuring the Burden of Major Cancers due to Premature Death in Korea. Journal of Preventive Medicine and Public Health. 33(2). 231–238.6 indexed citations
20.
Yoon, Seok‐Jun, et al.. (1997). Adoption and Its Determining Factors of Computerized Tomography in Korea. Journal of Preventive Medicine and Public Health. 30(1). 195–207.1 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.