Telemedicine in Arab Countries: Innovation, Research Trends, and Way Forward
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: The progress and innovation in telemedicine within the Middle Eastern countries have not been heavily monitored. Therefore, the present study aims to analyze the scholarly work conducted in the Arab world, using reproducible statistical and scientometric techniques. Methods: An electronic search of Web of Science (core database) had been conducted through use of an extensive search strategy comprising of keywords specific to the Arab region, EMRO countries, telehealth, medical conditions, and disorders. A total yield of 1,630 search results were processed, indexed through July 7, 2020. CiteSpace (5.7.R1, Drexel University, Pennsylvania, USA) is a Java-based application, a user-friendly tool for conducting scientometric analyses. Results: The present analyses found a lack of innovation in the field of digital health in the Arab countries. Many gaps in research were found in Arab countries, which will be discussed subsequently. Digital health research was clustered around themes of big data and artificial intelligence; a lack of progress was seen in telemedicine and digital health. Furthermore, only a small proportion of these publications had principal or corresponding authors from Arab countries. A clear disparity in digital health research in the Arab world was evident after comparing these insights with our previous investigation on telemedicine research in the global context. Conclusion: Telemedicine research is still in its infancy in the Middle Eastern countries. Recommendations include diversification of the research landscape and interdisciplinary collaborations in this area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it