Global Trends in Midwifery Education Research (2006–2025): A Systematic Bibliometric Review
DOI:
https://doi.org/10.59175/pijed.v4i2.760Keywords:
Bibliometric Analysis, Learning Models, Midwifery Education, Reflective PracticeAbstract
Scholarship on Midwifery Learning Models (MLMs) has expanded over the last two decades, yet no bibliometric synthesis has covered the entire 2006–2025 period; thus, the field’s intellectual roots, thematic shifts, and geographic spread remain incompletely mapped. To chart the intellectual, thematic, and geographic development of MLM research from 2006–2025. Methods: We conducted a systematic bibliometric review of SCOPUS-indexed publications using a structured TITLE-ABS-KEY search, multi-stage screening, and CSV/RIS export. The final corpus (n=456) was analyzed in VOSviewer and Biblioshiny to assess publication growth, citation impact, leading journals and authors, institutional and country networks, and conceptual structures (keyword co-occurrence, thematic mapping, and longitudinal evolution). Output increased after 2012, peaked in 2024 (45 articles), and fell in 2025 (25). Midwifery, Nurse Education Today, and Women and Birth were the most productive journals. Australia and the United Kingdom led productivity and citation impact, alongside other high-income settings. Core thematic clusters centered on reflective practice, clinical competence, continuity of care, and simulation, while AI-supported learning and COVID-19–responsive pedagogy emerged recently. Over time, MLM studies shifted from curriculum and clinical training foundations toward technology-integrated, student-centered, and simulation-enhanced models. This first 20-year bibliometric synthesis integrates structural and conceptual mapping to clarify how MLM scholarship has grown and diversified. Findings highlight the importance of scalable simulation, structured reflective learning, and continuity-of-care approaches, and stress capacity-building, equitable collaboration, and contextual adaptation in underrepresented regions. Future studies should also track implementation outcomes and learner equity as digital tools and AI mature across diverse health systems.
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