Research Trends of Health Professions Education Model from 2005 to 2024: A Bibliometric Analysis via CiteSpace
Bibliographic record
Abstract
Objective: This study seeks to offer medical educators, researchers, and clinical practitioners a novel, comprehensive, and visually engaging perspective on the field. It accomplishes this by analyzing the research trends, frontier topics, and pressing issues related to Health Professions Education Model (HPEM) over the past two decades from 2005 to 2024. Methods: This study employed CiteSpace 6.4.R1 and R-tool (version 4.5.1) software to conduct an analysis of articles pertaining to the Health Professions Education Model within the Web of Science Core Collection (WoSCC) database. The analysis encompassed various aspects, including keywords, disciplines, countries, institutions, authors, and references. Results: A total of 2,953 articles were incorporated into this study. The predominant research topics within this domain encompassed "interprofessional education (IPE)", "competency-based continuing professional development (CPD)", and "student-led professional experiences (SLEs)." The five countries with the highest number of publications related to Health Professions Education Model (HPEM) were the United States, Australia, Canada, England, and China. Notable authors in this field included Olle ten Cate, Frank Jason R. Eric S. Holmboe, and Carol Carraccio. Additionally, the journals that emerged as the leading publications in this discipline were Academic Medicine, Medical Education, and Medical Teacher. Conclusion: This study emphasizes the critical importance of international cooperation and exchange, particularly highlighting the necessity to strengthen collaboration among relevant institutions and prominent scholars in Europe, the United States of America, and China. Furthermore, it underscores current trends in the development of HPEM, specifically interprofessional education and competency-based continuing professional development. The research offers valuable insights for medical educators, scholars, and clinicians, enhancing their understanding of prevailing research trends and future directions within Health Professions Education Model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.041 | 0.120 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".