ReviewAdvances in medical education and practice2026
Thematic Evolution in Medical Education Research (2000-2024): A Large-Scale BERTopic Analysis.
Review in Advances in medical education and practice, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Few large-scale, semantically informed mappings of medical education research exist: traditional methods such as keyword co-occurrence and Latent Dirichlet Allocation cannot capture the contextual meaning of scientific abstracts. This study aimed to identify major research topics in medical education, classify their temporal trends, explore temporal associations with major policy and technology developments, and characterize their structural positioning. Methods: We applied BERTopic, a transformer-based topic modeling framework, to 276,253 English-language publications from PubMed, Web of Science, Scopus, and OpenAlex (2000-2024). Documents were embedded with a sentence-transformer language model and clustered into topics; labels were independently reviewed by two authors. Trends in each topic's relative share were classified using Poisson regression with an offset for total annual output, autocorrelation-robust Newey-West standard errors, and false discovery rate correction. Topic positioning used an embedding-based centrality-density heuristic. Results: BERTopic identified 39 topics (7.0% outliers); Topic 0 was a residual, non-substantive cluster excluded from interpretation. Among the 38 substantive topics, 10 showed significantly increasing relative shares (Hot; including AI/ChatGPT, virtual reality simulation, burnout and wellbeing, diversity and equity, empathy, and climate and health education), 12 showed decreasing shares (Cold), and 16 were Stable. Classifications agreed across alternative standard-error specifications for 35 of 38 topics; simulation-based education, clinical imaging, and interprofessional education were significant only under less conservative errors. Temporal associations with policy and technology reference periods were exploratory and not causal. The positional map distinguished established themes from emerging, terminologically unsettled niches. Conclusion: The relative emphasis of medical education research has gradually shifted toward technology-enhanced and socially responsive themes, while foundational topics continue to grow in absolute volume. These findings offer one data-driven, assumption-dependent lens on the field's evolution to inform curriculum planning, research prioritization, and policy.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.