Top-cited articles in medical professionalism: a bibliometric analysis versus altmetric scores

Abstract

IntroductionCitation counts of articles have been used to measure scientific outcomes and assess suitability for grant applications. However, citation counts are not without limitations. With the rise of social media, altmetric scores may provide an alternative assessment tool.ObjectivesThe aims of the study were to assess the characteristics of highly cited articles in medical professionalism and their altmetric scores.MethodsThe Web of Science was searched for top-cited articles in medical professionalism, and the characteristics of each article were identified. The altmetric database was searched to identify report for each identified article. A model to assess the relationship between the number of citations and each of the key characteristics as well as altmetric scores was developed.ResultsNo correlations were found between the number of citations and number of years since publication (p=0.192), number of institutes (p=0.081), number of authors (p=0.270), females in authorship (p=0.150) or number of grants (p=0.384). The altmetric scores varied from 0 to 155, total=806, median=5.0, (IQR=20). Twitter (54%) and Mendeley (62%) were the most popular altmetric resources. No correlation was found between the number of citations and the altmetric scores (p=0.661). However, a correlation was found for articles published in 2007 and after (n=17, p=0.023). To further assess these variables, a model was developed using multivariate analysis; did not show significant differences across subgroups. The topics covered were learning and teaching professionalism, curriculum issues, professional and unprofessional behaviour.ConclusionsAltmetric scores of articles were significantly correlated with citations counts for articles published in 2007 and after. Highly cited articles were produced mainly by the USA, Canada and the UK. The study reflects the emerging role of social media in research dissemination. Future studies should investigate the specific features of highly cited articles and factors reinforcing distribution of research data among scholars and non-scholars.