Research Hotspots and Trend Exploration on the Clinical Translational Outcome of Simulation-Based Medical Education: A 10-Year Scientific Bibliometric Analysis From 2011 to 2021
Bibliographic record
Abstract
Background In recent decades, an increasing number of studies have focused on the clinical translational effect of simulation-based medical education (SBME). However, few scientific bibliometric studies have analyzed the research hotspots and publication trends. This study aimed to investigate research hotspots and future direction in the clinical translational outcome of SBME via bibliometrics. Method Relevant publications on the clinical translational outcomes of SBME from 2011 to 2021 were identified and retrieved from the Web of Science Core Collection (WOSCC). Software including VOSviewer (1.6.17) and CiteSpace (5.8R3) and a platform ( bibliometric.com ) were employed to conduct bibliographic and visualized analysis on the literature. Results A total of 1,178 publications were enrolled. An increasing number of publications were observed in the past decades from 48 in 2011 to 175 in 2021. The United States accounted for the largest number of publications (488, 41.4%) and citations (10,432); the University of Toronto and Northwestern University were the leading institutions. Academic Medicine was the most productive journal concerning this field. McGaghie W C and Konge L were the most influential authors in this area. The hot topic of the translational outcome of SBME was divided into 3 stages, laboratory phase, individual skill improvement, and patient outcome involving both technical skills and non-technical skills. Translational research of comprehensive impact and collateral outcomes could be obtained in the future. Conclusion From the overall trend of 10 years of research, we can see that the research is roughly divided into three phases, from laboratory stage, individual skill improvement to the patient outcomes, and comprehensive impacts such as skill retention and collateral effect as cost-effectiveness is a major trend of future research. More objective evaluation measurement should be designed to assess the diverse impact and further meta-analysis and randomized controlled trials are needed to provide more clinical evidence of SBME as translational science.
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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 | 0.020 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.143 | 0.175 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".