Objective To understand the research fronts, the status of China′s international scientific research cooperation, and the distribution of the Chinese authors with outstanding contributions in the field of medical education. Methods Based on the Web of Science database, with medical education as the theme, CiteSpace and HistCite were used for co-citation cluster analysis and external characteristic analysis of the included literature. Results In 2023, the research fronts of medical education focus on seven themes, including equity of medical education, reform of medical teaching mode in the post-COVID era, entrustable professional activities, professional identity formation, educational technology and anatomy teaching, application of artificial intelligence, and reconstruction of medical education assessment. From 2014 to 2023, Chinese authors published 1 703 papers on medical education, including 465 (27.3%) were papers with international research cooperation, which involved 90 cooperating countries or regions. There was a statistically significant difference in the number of citations per paper between international and non-international collaborative papers, 4.0 (1.0, 10.0) vs. 2.0 (1.0, 2.0), (Z=-6.75, P<0.001). The contribution value of the publications ranges from 1.8 to 6.8, and the contribution value of the citations ranges from 15.4 to 246.9 for Chinese authors with outstanding contributions. Conclusions The application of artificial intelligence and the reconstruction of medical education evaluation become the emerging research fronts of medical education in 2023. The participation of Chinese authors in international scientific research cooperation in medical education is still insufficient, and the comprehensive contribution value of Chinese authors with outstanding contributions needs improvement.
[1] 闻德亮, 李鸿鹤, 宋鑫智. 基于科学引文数据库的医学教育研究前沿分析(2023)[J].中华医学教育杂志,2023,43(12):881-886. DOI: 10.3760/cma.j.cn115259-20230331-00349.
[2] 吴红斌, 杜鹃,程化琴, 等. 国际医学教育研究发展及启示 ——基于WOS百余年文献的计量分析[J].复旦教育论坛,2019,17(5):97-103. DOI: 10.3969/j.issn.1672-0059.2019.05.017.
[3] 中国科协学会服务中心. 科睿唯安发布《2023中国国际科研合作现状报告》[EB/OL].(2024-03-15)[2024-03-31]. https://stm.castscs.org.cn/yw/40694.jhtml.
[4] Elsevier, 中国教育在线. 重磅！2023“中国高被引学者”榜单出炉[EB/OL].(2024-03-28)[2024-03-31]. https://baijiahao.baidu.com/s?id=1794723847993644849&wfr=spider&for=pc.
[5] Hauer KE, Park YS, Bullock JL, et al. “My assessments are biased!” measurement and sociocultural approaches to achieve fairness in assessment in medical education[J]. Acad Med, 2023,98(8S):S16-S27. DOI: 10.1097/ACM.0000000000005245.
[6] Anderson N, Nguyen M, Marcotte K, et al. The long shadow: a historical perspective on racism in medical education[J]. Acad Med, 2023,98(8S):S28-S36. DOI:10.1097/ACM.0000000000005253.
[7] Mabeza RM, Christophers B, Ederaine SA, et al. Interventions associated with racial and ethnic diversity in US graduate medical education: a scoping review[J]. JAMA Netw Open, 2023,6(1):e2249335. DOI: 10.1001/jamanetworkopen.2022.49335.
[8] Zhang P, Li X, Pan Y, et al. Global trends and future directions in online learning for medical students during and after the COVID-19 pandemic: a bibliometric and visualization analysis[J]. Medicine (Baltimore), 2023,102(50):e35377. DOI: 10.1097/MD.0000000000035377.
[9] Gardanova Z, Belaia O, Zuevskaya S, et al. Lessons for medical and health education learned from the COVID-19 pandemic[J]. Healthcare (Basel), 2023,11(13):1921. DOI: 10.3390/healthcare11131921.
[10] Agarwal A, Subramaniam G, Khattak O, et al. Navigating post COVID-19 education: an investigative study on students′ attitude and perception of their new normal learning environment[J]. PeerJ, 2023,11:e16184. DOI: 10.7717/peerj.16184.
[11] Dimassi Z, Chaiban L, Zgheib NK, et al. Re-conceptualizing medical education in the post-COVID era[J]. Med Teach, 2024,46(8):1084-1091. DOI: 10.1080/0142159X.2023.2290463.
[12] Lee MC, Melcer EF, Merrell SB, et al. Usability of entrust as an assessment tool for entrustable professional activities (EPAs): a mixed methods analysis[J]. J Surg Educ, 2023,80(11):1693-1702. DOI: 10.1016/j.jsurg.2023.09.001.
[13] Krecko LK, Jung S, Martin S, et al. Enhancing the value of surgical entrustable professional activities through integrative learning analytics[J]. J Surg Educ, 2023,80(10):1370-1377. DOI: 10.1016/j.jsurg.2023.07.018.
[14] Charondo LB, Sheu L, Bakke BM, et al.‘It′s more like checking in with an old friend′: a qualitative study of medical students′ experiences with longitudinal coaches throughout medical school[J]. Med Teach, 2024,46(6):808-816. DOI: 10.1080/0142159X.2023.2284659.
[15] Gauthier S, Braund H, Dalgarno N, et al. Assessment-seeking strategies: navigating the decision to initiate workplace-based assessment[J]. Teach Learn Med, 2023:1-10. DOI: 10.1080/10401334.2023.2229803.
[16] Thomas T, Arif S, Franklin CJ, et al. The intersection of professional identity formation, bias, and marginalized identities[J]. Am J Pharm Educ, 2023,87(11):100546. DOI: 10.1016/j.ajpe.2023.100546.
[17] Koh E, Koh KK, Renganathan Y, et al. Role modelling in professional identity formation: a systematic scoping review[J]. BMC Med Educ, 2023,23(1):194. DOI: 10.1186/s12909-023-04144-0.
[18] Aluri J, Ker J, Marr B, et al. The role of arts-based curricula in professional identity formation: results of a qualitative analysis of learner′s written reflections[J]. Med Educ Online, 2023,28(1):2145105. DOI: 10.1080/10872981.2022.2145105.
[19] Veen M, de la Croix A. How to grow a professional identity: philosophical gardening in the field of medical education[J]. Perspect Med Educ, 2023,12(1):12-19. DOI: 10.5334/pme.367.
[20] Pereda-Nuñez A, Manresa M, Webb SS, et al. Pelvic + anatomy: a new interactive pelvic anatomy model. Prospective randomized control trial with first-year midwife residents[J]. Anat Sci Educ, 2023,16(5):843-857. DOI: 10.1002/ase.2304.
[21] Vasil′ev YL, Dydykin SS, Kashtanov AD, et al. A comparative analysis of lecturers′ satisfaction with anatomage and pirogov virtual dissection tables during clinical and topographic anatomy courses in Russian universities[J]. Anat Sci Educ, 2023,16(2):196-208. DOI: 10.1002/ase.2248.
[22] Khan J, Baatjes KJ, Layman-Lemphane JI, et al. Online anatomy education during the Covid-19 pandemic: opinions of medical, speech therapy, and BSc anatomy students[J]. Anat Sci Educ, 2023,16(5):892-906. DOI: 10.1002/ase.2271.
[23] Elston P, Canale GP, Ail G, et al. Twelve tips for teaching in virtual reality[J]. Med Teach, 2024,46(4):495-499. DOI: 10.1080/0142159X.2023.2285396.
[24] Zumsteg JM, Junn C. Will ChatGPT match to your program?[J]. Am J Phys Med Rehabil, 2023,102(6):545-547. DOI: 10.1097/PHM.0000000000002238.
[25] Riedel M, Kaefinger K, Stuehrenberg A, et al. ChatGPT′s performance in German OB/GYN exams - paving the way for AI-enhanced medical education and clinical practice[J]. Front Med (Lausanne), 2023,10:1296615. DOI: 10.3389/fmed.2023.1296615.
[26] Choi W. Assessment of the capacity of ChatGPT as a self-learning tool in medical pharmacology: a study using MCQs[J]. BMC Med Educ, 2023,23(1):864. DOI: 10.1186/s12909-023-04832-x.
[27] Russell RG, Lovett Novak L, Patel M, et al. Competencies for the use of artificial intelligence-based tools by health care professionals[J]. Acad Med, 2023,98(3):348-356. DOI: 10.1097/ACM.0000000000004963.
[28] Buabbas AJ, Miskin B, Alnaqi AA, et al. Investigating students′ perceptions towards artificial intelligence in medical education[J]. Healthcare (Basel),2023,11(9):1298. DOI: 10.3390/healthcare11091298.
[29] Moodi Ghalibaf A, Moghadasin M, Emadzadeh A, et al. Psychometric properties of the persian version of the medical artificial intelligence readiness scale for medical students (MAIRS-MS)[J]. BMC Med Educ, 2023,23(1):577. DOI: 10.1186/s12909-023-04553-1.
[30] Ng F, Thirunavukarasu AJ, Cheng H, et al. Artificial intelligence education: an evidence-based medicine approach for consumers, translators, and developers[J]. Cell Rep Med, 2023,4(10):101230. DOI: 10.1016/j.xcrm.2023.101230.
[31] Gutiérrez-Cirlos C, Carrillo-Pérez DL, Bermúdez-González JL, et al. ChatGPT: opportunities and risks in the fields of medical care, teaching, and research[J]. Gac Med Mex, 2023,159(5):372-379. DOI: 10.24875/GMM.M23000811.
[32] Tolsgaard MG, Pusic MV, Sebok-Syer SS, et al. The fundamentals of artificial intelligence in medical education research: AMEE Guide No. 156[J]. Med Teach, 2023,45(6):565-573. DOI: 10.1080/0142159X.2023.2180340.
[33] Valentine N, Durning SJ, Shanahan EM, et al. What stops fairness from emerging in assessment? the forces on a complex adaptive system[J]. Perspect Med Educ, 2023,12(1):338-347. DOI: 10.5334/pme.994.
[34] Malau-Aduli BS, Hays RB, D′Souza K, et al. Twelve tips for improving the quality of assessor judgements in senior medical student clinical assessments[J]. Med Teach, 2023,45(11):1228-1232. DOI: 10.1080/0142159X.2023.2216364.
[35] Boursicot K, Kemp S, Norcini J, et al. Synthesis and perspectives from the Ottawa 2022 conference on the assessment of competence[J]. Med Teach, 2023,45(9):978-983. DOI: 10.1080/0142159X.2023.2174420.
[36] Heng J, Teo DB, Tan LF. The impact of chat generative pre-trained transformer (ChatGPT) on medical education[J]. Postgrad Med J, 2023,99(1176):1125-1127. DOI: 10.1093/postmj/qgad058.
[37] Gordon M, Daniel M, Ajiboye A, et al. A scoping review of artificial intelligence in medical education: BEME Guide No. 84[J]. Med Teach, 2024,46(4):446-470. DOI: 10.1080/0142159X.2024.2314198.
