Trends in patient safety education research for healthcare professional students over the past two decades: a bibliometric and content analysis
Bibliographic record
Abstract
Research and practice in patient safety education have garnered widespread attention; however, a comprehensive bibliometric analysis is lacking. This study aimed to provide a comprehensive understanding of the research focus and research trends in the globalization of the field of patient safety education and to describe the general characteristics of publications. Data on articles and reviews about student safety education were extracted from Web of Science. Microsoft Excel 2019, CiteSpace 6.1.R3, VOSviewer 1.6.18, SATI 3.2, Scimago Graphica, and Pajek were used for quantitative analysis. Collaboration networks of countries, institutions, journals, authors, and keywords were visualized based on publications from January 2000 to September 2022. A total of 573 papers were published between 2000 to 2022, showing an overall increasing trend. The USA, England, and Australia are the top three most prolific countries; Johns Hopkins University, the University of Technology Sydney, and the University of Toronto are the top three most productive institutions; Nurse Education Today, Journal of Nursing Education, and BMC Medical Education are the most productive journals; Based on content analysis five research hotspots focused on: (1) Quality Improvement of Patient safety Teaching and Learning; (2) Patient safety Teaching Content; (3)Specialized Teaching in Patient Safety; (4) Integrating Patient Safety and Clinical Teaching; (5)Patient Safety Teaching Assessment Content. Through keyword clustering analysis, five research hotspots and relevant contents were identified. According to this study, simulation, communication, collaboration, and medication may attract more attention from researchers and educators, and could be the major trend for future study.
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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 | 0.016 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.175 | 0.245 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".