Global Research Activity on E-Learning in Health Sciences Education: a Bibliometric Analysis
Bibliographic record
Abstract
Progress in electronic learning (e-learning) and health sciences education is an indicator of the national and international efforts to achieve sustainable development goals regarding good health and quality education. The objective of the current study was to describe research volume and trends on e-learning in the health sciences education. A bibliometric methodology was adopted. The study period was from database inception until December 31, 2020. The data was downloaded from Scopus as a “csv” file. The data was analyzed to reveal prominent contributing countries, institution, authorship patterns, the degree of collaboration, international research collaboration, prominent sources for publications, frequent author keywords, the impact of research in terms of citations, and healthcare groups targeted in research. In total, 4576 records were retrieved. The analysis revealed an increasing growth in number of publications with time. There was a sharp peak in 2020. Recent literature on e-learning in health education included keywords such as flipped classroom, mobile learning, blended learning, and COVID-19. Countries in the European region and the region of the Americas have the highest contribution while countries in the African and the South-East Asian region have the least contribution. There was an increasing trend in the degree of author collaboration with time. However, the extent of international research collaboration was inadequate. The USA had the least percentage of documents with international authors (18%) while Sweden had the highest (70.6%). Documents published from Canada had the highest number of citations per document. The Karolinska Institute , based in Sweden, was the most active institution. The Medical Teacher journal ranked first in the number of publications while documents published in the Academic Medicine journal received the highest number of citations per document. The bulk of the retrieved literature was about medical or nursing education. The retrieved documents had an average of 12.7 citations per document and an H-index of 81. Data presented can be used to develop and enhance e-learning in health sciences education in regions with poor research contribution. Policies regarding open access publications, international research collaboration, and adoption of e-learning methodologies in low- and middle-income countries need to be endorsed.
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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 | 0.011 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.170 | 0.279 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".