Visual Analysis of the Current State of Research on the Use of Games in Medical Education
Bibliographic record
Abstract
Background Games are an emerging pedagogical tool in medical education, and they are recognized as a potentially attractive form of complementary learning. Research on the application of games in medical education has gradually increased in recent years, but the development of related research in China has been slow. Reviewing the research literature related to the use of games in medical education and gaining a comprehensive understanding of the current status and hotspots of the development of this field will be conducive to promoting the development of the research field of medical education in China. Objective To analyze the current status of the research on the application of games to medical education, and to provide reference and information for future research related to the application of games to medical education. Methods From February to April in 2023, with the help of CiteSpace 6.1R6 and Microsoft Excel 2019 software, we searched the relevant literature from Web of Science Core Collection database (WoSCC) from 2013-01-01 to 2023-02-26 with the theme of "game" and "medical education", and analyzed the number of publications, authors, countries/institutions, and the number of articles. Results Finally, 652 English-language articles were included. There was a general upward trend in the number of publications in the last 11 years, with the United States being the top country in terms of the number of publications (201), centrality (0.48), and the annual number of publications, and Canada (67, centrality 0.15) and the United Kingdom (56, centrality 0.47) in the second and third places, respectively. The University of Toronto, Canada was the institution with the highest total number of articles with 21 articles. High-frequency keywords included medical education, education, serious games, simulation, and performance. The keyword with the highest level of emergence in the keyword emergence analysis was serious games (emergence intensity 3.4) . Conclusion Currently, the application of games in medical education has attracted more and more attention from scholars, and the research hotspots mainly focus on the role played by different games in the process of talent cultivation, but there is no high-quality evidence to provide educators with evidence-based recommendations. China still needs further exploration in this field, and educators need to apply more in the teaching process to better validate its effectiveness and promote the development of medical education.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.053 | 0.043 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.002 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".