Trends in patient safety education research for healthcare professional students over the past two decades: a bibliometric and content analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.013 | 0.035 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it