Bibliometric Analysis Identifies Publication Trends and Most Common Research Topics Related to Internet Health Information Seeking Behaviour

A Review of: Li, F., Li, M., Guan, P., Ma, S., & Cui, L. (2015). Mapping publication trends and identifying hot spots of research on Internet health information seeking behavior: A quantitative and co-word biclustering analysis. Journal of Medical Internet Research, 17(3), e81. http://dx.doi.org/10.2196/jmir.3326 Objective – To identify research and publication trends related to health-seeking information behaviour on the Internet. Design – Bibliometric analysis, publication trends, and co-word biclustering analysis. Setting – Academic journals. Subjects – Journal articles retrieved from PubMed meeting eligibility criteria, and articles selected through hand-searching of the top three journals publishing in the identified area of research. Methods – A search for relevant articles was performed in PubMed and supplemented by manual searching of the top three journals in the field, yielding a total of 2,780 articles. Following a high concordance rate on screening agreement, researchers identified a total of 533 articles for inclusion. These articles were considered to be representative of all the articles published on Internet health-seeking behaviour as of September 2014. Data deemed essential to biclustering co-word analysis included article title, author, institution, country, source, publication year, and MeSH terms, and was collected in both XML and MEDLINE formats to ensure information exhaustivity for subsequent analysis. Analysis of the distribution of data, as well as major MeSH frequency ranking, allowed researchers to identify the most active journals in the subject area, while biclustering for highly frequent MeSH terms determined hot spots of research. Researchers used both mountain and matrix visualization to further illustrate semantic relationships of MeSH terms and the framework for the analysis of research hot spots. Co-word analysis facilitated the identification of like-articles based on major MeSH indexing, while cluster analysis utilized a matrix grouping to identify themes. By combining this information and reorganizing the matrix, researchers were able to highlight the most common themes. Main Results – Researchers identified ten research “hot spots,” the most prolific research topics, thus providing the top subject areas of research published in the literature related to Internet health-seeking behaviour. Top subjects include health information seeking behaviour related to HIV infection or sexually transmitted diseases; information seeking behaviour of students and of patients with cancer; consumer trust in online health information; behaviour of Internet health information seeking through mobile apps; the interaction between physician-patient relations/communications and Internet use; personal preference and computer literacy related to Internet use; and the use of social media by parents. In terms of publishing rates, the number of papers published on health information seeking behaviour has increased consistently since 1985, when only one paper was published, to 2013, in which 114 papers were published. Authors from 42 countries or regions contributed to the body of relevant literature, with authors from the United States of America accounting for over half of published papers. Just over 96% of articles were published in English. Of the 253 journals identified as publishers of these articles, eight published over one-third of all the identified articles. The Journal of Medical Internet Research published the most articles on this topic. Conclusion – Bibliographic analysis identified both subject and publication trends related to Internet health information seeking behaviour. Publication rates of research in the area of Internet health information have increased steadily since the first article was published in 1985. The bulk of the research tends to fall within ten identified hot spots, or research topics, according to a bibliometric analysis of indexing.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.