Analysis of Co-authorship Patterns in Global e-Health Literacy Research
Bibliographic record
Abstract
Rationale of the Study - This study investigated the co-authorship patterns in global research on e-health literacy, focusing on authors, countries, and organisations to reflect the participation of African researchers and their institutions in collaborative research on e-health literacy. Methodology - The study period covered 2006 to 2024 and was based on publications retrieved from Scopus. VOSviewer was used to identify and map co-authored documents, revealing valuable insights into authorship. Findings - Between 2006 and 2024, a total of 7,887 authors wrote 2,027 documents on the subject, resulting in an average of 415 authorships per year. Leading countries in co-authorship include the US, Germany, Australia, and Canada. There were only 12 connected organisations, indicating room for growth in inter-institutional research partnerships. The University of Education, Winneba, Ghana, was the only African institution to have published four documents and established four collaborative links. Implications - Expanding institutional partnerships, particularly with countries that have high research volumes, such as China, could help address the disparity between research quantity and citation impact. Promoting structured collaborations can facilitate resource sharing, diversify perspectives, and improve the overall quality and influence of research, driving innovation and advancing the field globally. Originality - Many studies in this field focused on quantitative research, but this study deploys bibliometrics to reveal important insights. A unique finding of the study is a comparatively high volume of Chinese research. Conversely, previous studies have shown that China has a low research impact, highlighting disparities in global research influence.
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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 | 0.015 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.026 | 0.034 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 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".