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. 2024 Feb 21;34(3):551-560.
doi: 10.1007/s40670-024-02003-1. eCollection 2024 Jun.

Problem-Based Learning in Medical Education: A Global Research Landscape of the Last Ten Years (2013-2022)

Jéssica de Andrade Gomes  1 , Luiza Amara Maciel Braga  1 , Bernardo Pereira Cabral  1   2 , Renato Matos Lopes  1 , Fabio Batista Mota  1
Affiliations

Problem-Based Learning in Medical Education: A Global Research Landscape of the Last Ten Years (2013-2022)

Jéssica de Andrade Gomes et al. Med Sci Educ. .

Abstract

Background: Problem-based learning (PBL) constructs a curriculum that merges theory and practice by employing clinical scenarios or real-world problems. Originally designed for the pre-clinical phase of undergraduate medicine, PBL has since been integrated into diverse aspects of medical education. Therefore, this study aims to map the global scientific landscape related to PBL in medical education in the last ten years.

Methods: We combined bibliometrics and network analysis to analyze the metadata of related research articles published between 2013 and 2022 and indexed in the Web of Science Core Collection.

Results: Our results show an annual publication rate of 9.42%. The two main journals disseminating research on this subject are BMC Medical Education and Medical Teacher. Education & Educational Research and Health Care Sciences & Services are the two most frequent research areas, and also the two most central nodes of the related network. The USA and China are the most publishing countries, while the Netherlands and Canada are the most collaborative. The Maastricht University holds the position of most publishing and collaborative research organization. The University of California ranks second in publications, while the University of Toronto is the second most central research organization.

Conclusions: Our study provides an overview of the last ten years of publications related to PBL and medical education, and we hope it can be of interest to educators, researchers, and students involved with this subject.

Supplementary information: The online version contains supplementary material available at 10.1007/s40670-024-02003-1.

Keywords: Bibliometrics; Medical education; Network analysis; Problem-based learning; Scientific publication.

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Conflict of interest statement

Competing InterestsThe authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Publication years and journals (2013–2022). a Evolution of annual publications. b Journals with four or more articles
Fig. 2
Fig. 2
Research areas (2013–2022). a Research areas with frequency greater than or equal to 4. b Undirected network of research areas consisting of 43 nodes with frequency greater than or equal to 4 and 39 edges. Only nodes with DC higher than the network average (1.814) were labeled. Node sizes and colors according to WDC—the larger the node and the darker its color (darkening towards blue), the higher its WDC. Edge thickness according to the number of co-occurrences between nodes. The Fruchterman-Reingold algorithm provides the layout of the network
Fig. 3
Fig. 3
Countries and organizations (all years—2013–2022). a Countries with four or more articles. b Undirected network of research areas consisting of 43 nodes with a frequency greater than or equal to 3 and 51 edges. c Universities with a frequency greater than or equal to 3. d Undirected network of organizations consisting of 22 nodes with a frequency equal to or greater than 3 and 16 edges. In both networks (b and d), node sizes and colors are according to WDC—the larger the node and the darker its color (darkening towards blue), the higher its WDC. Edge thickness according to the number of co-occurrences between nodes. The Fruchterman-Reingold algorithm provides the layout of the two networks
Fig. 4
Fig. 4
Keywords (all years—2013–2022). a Keywords with four or more articles. b Undirected network of keywords consisting of 43 nodes with a frequency greater than or equal to 4 and 55 edges. Node sizes and colors according to WDC—the larger the node and the darker its color (darkening towards blue), the higher its WDC. Edge thickness according to the number of co-occurrences between nodes. The Fruchterman-Reingold algorithm provides the layout of the network
See this image and copyright information in PMC

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