Global trends and hotpots in standardised patients research in the last 30 years: A bibliometric analysis
Bibliographic record
Abstract
Background: The research trends regarding standardised patients(SPs) in the education of health professions students have not been systematically studied. Methods: All published literature on SPs from January 1994 to January 2024 in Web of Science was screened by two reviewers. Bibliometric analysis and knowledge mapping visualisation analysis were performed using Cite Space software. The country, institution, journal, keyword co-occurrence, and keyword emergence were visualised. Result: A total of 3259 records were analysed. The amount of relevant literature in the past 30 years showed an upward trend involving 109 disciplinary categories, with the United States dominating. The five central research teams were from the United States and Canada. Nursing education is increasingly using SPs, especially in advanced nursing practice. As for the hotspot and trend analysis, the results indicate that there is still continuous attention to the impact of applying standardised patients on improving the communication ability, competence and performance of medical students. Additionally, there is a growing interest in exploring the application of visual simulation or artificial intelligence in standardised patient-related research. Conclusions: Research on SPs' has received continued attention. To cater to the diverse requirements of education and clinical context, there is a need for further exploration of SPs utilisation. AI-relevant SPs might be a new alternative for various scenarios in the future.
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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 | 0.022 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.144 | 0.195 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| 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".