- Impact factor surge in Korean medical journals during the COVID-19 era: a bibliometric study
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Chansu Park, Sejin Park, Hyeonseok Seo, Janghyeog Oh, Dongryeong Kim, Junha Kang, Hanul Kang, Hyunsung Kang, Yaechan Kim, Mi Ah Han
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Sci Ed. 2024;11(1):55-61. Published online December 18, 2023
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DOI: https://doi.org/10.6087/kcse.320
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Abstract
PDFSupplementary Material
- Purpose
The multiyear COVID-19 pandemic has affected the volume and speed of publications in scientific journals. This study evaluated trends in the impact measures of international medical journals published in Korea, including the journal impact factor (JIF).
Methods We selected Science Citation Index Expanded journals with the country/region set to Korea and the academic category classified as “clinical medicine” in Journal Citation Reports. Trends in indicators such as the JIF and Journal Citation Indicator (JCI) were assessed for journals with JIF information from 2018 to 2022. Ratios and differences between the measures were calculated to determine the extent of the change.
Results We identified 43 journals, and the average JIF of those journals increased from 2.33 in 2018 and 2.50 in 2019 to 3.45 in 2020 and 3.86 in 2021. Other measures, such as the 5-year JIF and JCI, steadily increased, and the proportion of gold open access journals also increased significantly. However, the JCI and Eigenfactor scores remained steady or showed relatively small increases. Furthermore, impact measures declined in 2022, including a JIF decrease to 3.55.
Conclusion We presented trends in quantitative measurements for international medical journals in Korea, and found an overall increase. Journals need to maintain a rigorous publication process to improve the quality of their research and the research community needs to exercise caution when using quantitative measures to evaluate journals. Further research is required to examine the quantitative indicators of journals, including their publication policies, research topics, and long-term trends.
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- Scientific Publication Speed of Korean Medical Journals during the COVID-19 Era
Hyeonseok Seo, Yaechan Kim, Dongryeong Kim, Hanul Kang, Chansu Park, Sejin Park, Junha Kang, Janghyeog Oh, Hyunsung Kang, Mi Ah Han Healthcare Informatics Research.2024; 30(3): 277. CrossRef
- How authors select covariates in the multivariate analysis of cancer studies in
10 oncology journals in Korea: a descriptive study
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Mi Ah Han, Hae Ran Kim, Sang Eun Yoon, Sun Mi Park, Boyoung Kim, Seo-Hee Kim, So-Yeong Kim
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Sci Ed. 2024;11(1):26-32. Published online February 20, 2024
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DOI: https://doi.org/10.6087/kcse.327
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Abstract
PDFSupplementary Material
- Purpose
Cancer is the leading cause of death in Korea, leading many investigators to focus on cancer research. We present the current practice of variable selection methods for multivariate analyses in cancer studies recently published in major oncology journals in Korea.
Methods We included observational studies investigating associations between exposures and outcomes using multivariate analysis from 10 major oncology journals published in 2021 in KoreaMed, a Korean electronic database. Two reviewers independently and in duplicate performed the reference screening and data extraction. For each study included in this review, we collected important aspects of the variable selection methods in multivariate models, including the study characteristics, analytic methods, and covariate selection methods. The descriptive statistics of the data are presented.
Results In total, 107 studies were included. None used prespecified covariate selection methods, and half of the studies did not provide enough information to classify covariate selection methods. Among the studies reporting selection methods, almost all studies only used data-driven methods, despite having study questions related to causality. The most commonly used method for variable selection was significance in the univariate model, with the outcome as the dependent variable.
Conclusion Half of the included studies did not provide sufficient information to assess the variable selection method, and most used a limited data-driven method. We believe that the reporting of covariate selection methods requires improvement, and our results can be used to educate researchers, editors, and reviewers to increase the transparency and adequacy of covariate selection for multivariable analyses in observational studies.
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