Social media in nursing and midwifery education: A 20-year bibliometric analysis
Bibliographic record
Abstract
AIM: To provide insights into scientific publications, research trends and knowledge gaps on social media in nursing and midwifery education BACKGROUND: Social media is widely used in nursing and midwifery education to support learning. DESIGN: Bibliometric analysis METHODS: Scopus was searched using key terms (2004-2023). Results were screened on Rayaan for relevancy leaving 481 studies. Microsoft Excel and VOSviewer aided the bibliometric analysis to understand the volume and scope of research on social media in nursing and midwifery education. RESULTS: Pedagogical research on social media in nursing and midwifery increased steadily since 2004, with a slight decrease in 2022 possibly due to the coronavirus pandemic. The countries which published most in the field were the United States, the United Kingdom, Australia and Canada and their institutions and researchers had numerous co-authorship links with others across the globe. Six main research themes emerged - 1) diversity of social media, 2) learning on social media, 3) impact of social media during COVID-19, 4) professionalism on social media, 5) interprofessional education and 6) pedagogy in social media education. CONCLUSION: Pedagogical research on social media in nursing and midwifery education is growing. This evidence can help educators and students make the most of these dynamic technologies for learning. Further research into newer online platforms such as Instagram, TikTok and LinkedIn, exploring social media for the continuing professional development of nurses and midwives and more rigorous experimental research examining the effect these technologies have on the learning outcomes of students and practitioners to inform educational practice.
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How this classification was reachedexpand
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 | 0.002 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.040 | 0.146 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".