Bibliographic record
Abstract
Purpose This study aims to examine the volume of ehealth literacy documents during 2006–2022, and the nature of citation of ehealth documents by country, organizations, sources and authors. Design/methodology/approach The study adopted a bibliometric approach. Bibliographic data was collected on citation of ehealth documents by country, organizations, sources and authors from Scopus and mapped and visualized the citations using VosViewer. Findings A total of 1,176 documents were produced during 2006–2022, indicating a high rate of document production in this sub-discipline. Among the 102 countries that contributed documents on the subject, 58 qualified for the analysis. The USA had the highest number of cited documents on eHealth literacy, followed by Canada and Australia. The average publication year for the USA was 2018, with 348 publications and an average of 24.12 citations. Canada had a high average citation count of 44.69. Furthermore, the document examined citations by organizations. Research limitations/implications The research implications of the study suggest that eHealth literacy is an actively growing field of research, with a substantial impact on the academic community, and researchers should focus on collaboration with high-impact institutions and journals to increase the visibility and recognition of their work, while also paying attention to the need for more research representation from African countries. Practical implications The study’s findings indicate a high rate of document production and growing interest in eHealth literacy research, with the USA leading in the number of cited documents followed by Canada, while Canadian eHealth literacy research receives relatively higher citation rates on average than the USA. Originality/value The study’s originality lies in its examination of citation patterns and global contributions to eHealth literacy literature, offering valuable insights for researchers. It identifies key authors, high-impact journals and institutions, providing valuable guidance for collaboration. The research highlights a growing interest in eHealth literacy, underscoring its potential impact on public health and digital health interventions.
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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 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.074 | 0.160 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".