Moving towards decolonizing health professions education research: an analysis of global network patterns and equity implications

Abstract

Abstract Colonialism has left a lasting remnant in global academic community, with research contributions and authorship historically concentrated among high-income countries. This imbalance has made decolonization in research a global agenda aimed at rebalancing power relations across the community. Nevertheless, traditional bibliometric tools remain unable to capture such underlying power dynamics. This study proposes leveraging social network analysis (SNA) to examine community architecture and promote inclusivity in health professions education (HPE) research. We analyzed peer-reviewed articles published between 2015 and 2024 in leading HPE journals, extracting country-level co-authorship data from Scopus. SNA metrics, particularly small world coefficient and centrality, were used to assess collaboration structures, integration patterns, and power dynamics, capturing how power is distributed across the network. The analysis revealed an expanding–centralized pattern: although the research community has expanded globally, with more countries contributing, the overall network structure has remained hierarchical. The network’s small-worldness declined, indicating growing concentration of power within the network core, while stable centrality showed that dominant players have remained consistent throughout the decade. Despite an increasing number of nodes suggesting broader participation, power became increasingly centralized rather than distributed toward the periphery, suggesting the global network remains structurally inequitable and epistemic colonization may still persist. Systemic reforms in research infrastructure, nationally and globally, and in journal policies are needed to enable greater inclusivity. SNA offers critical insight into the structural imbalances that must be monitored to guide process toward achieving an equitable global research ecosystem.

The authors' abstract, as published at the source. Scientometrics, 2026 · DOI ↗