Assessment and feedback research in medical education (2000–2024): A bibliometric analysis of a topic-model-delineated corpus
Chujie ChenAbstract
Background:
Quantitative maps of assessment and feedback research in medical education have largely focused on subsets such as artificial intelligence and objective structured clinical examination (OSCE).
Aim:
The aim of the study was to quantify the scale, collaboration, knowledge base and thematic evolution of this literature from 2000 to 2024 within one corpus.
Materials and Methods:
From a four-database medical education library, a topic-model-delineated corpus of 15,287 records was analysed using negative-binomial trend analysis with field-share estimation, collaboration networks, co-citation analysis, keyword co-occurrence and burst detection, Bradford and Lotka distributions and sub-topic clustering. Corpus membership was validated using two language models.
Results:
Annual output grew from 233 publications in 2000 to 899 in 2024, whereas the corpus’s share of the medical education literature showed no detectable average linear change (incidence rate ratio: 1.002 per year, 95% confidence interval: 0.997–1.008). Production was concentrated in the United States, the United Kingdom and Canada, and 23 journals supplied one-third of the journal-mapped output. Co-citation analysis revealed an intellectual base anchored by classic assessment and feedback theory, and title–keyword bursts showed the research front shifting from clinical competence and standardised patients towards entrustable professional activities, programmatic assessment, equity and well-being (2020–2024). Detected psychometric terminology showed no trend, at 25.3% versus 6.8% of assessment-theme and feedback-theme records, respectively.
Conclusion:
This literature is established, and its internal agenda has shifted towards entrustment and learner well-being, whereas its share of the parent literature showed no detectable average change at the boundary analysed.