Knowledge mapping of online healthcare: An interdisciplinary visual analysis using VOSviewer and CiteSpace
Bibliographic record
Abstract
Background: Online healthcare has been regarded as a permanent component and complementation in routine worldwide healthcare. Although there have been large-scale related studies in this field, studies are scattered across disciplines. Numerous publications are needed to systematically and comprehensively identify the status quo, development, and future hotspots in this field. Methods: Publications on online healthcare were screened from the WoS database. By using VOSviewer and CiteSpace, this study analyzed 4636 articles in this field with 60,306 associated references. First, countries/regions distributions, institutions distributions, influential journals, and productive authors were obtained. Then, co-citation analysis, co-occurrence analysis, timeline analysis, and burst detection were further conducted to sketch the panorama of online healthcare. Results: There were 147 countries/regions participated in and contributed to this field in total. Accounting for over half of the total number of publications, the USA, England, Australia, China, and Canada played significant roles in this area. Among the 24,362 authors, Guo XT was the most influential author. The International Journal of Environmental Research and Public Health was the journal with the most publications and citations. Studies in this field have shifted from basic research to applied practice research. COVID-19, mental health, healthcare, and healthcare workers were the most common keywords, indicating that studies on the impact of online healthcare on healthcare workers, online healthcare service for COVID-19, and mental health will be promising areas in the future. Conclusions: Research on online healthcare is booming, while worldwide cooperation is still regionalized. Cross-regional cooperation among institutions and scholars is needed to enhance in the future. Online healthcare services for specific health fields and specific groups are the current and developing topics in this field.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.080 | 0.053 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".