Categorizing the Telehealth Policy Response of Countries and Their Implications for Complementarity of Telehealth Policy
Bibliographic record
Abstract
Developing countries are exploring the role of telehealth to overcome the challenges of providing adequate health care services. However, this process faces disparities, and no complementarity in telehealth policy development. Telehealth has the potential to transcend geopolitical boundaries, yet telehealth policy developed in one jurisdiction may hamper applications in another. Understanding such policy complexities is essential for telehealth to realize its full global potential. This study investigated 12 East Asian countries that may represent a microcosm of the world, to determine if the telehealth policy response of countries could be categorized, and whether any implications could be identified for the development of complementary telehealth policy. The countries were Cambodia, China, Hong Kong, Indonesia, Japan, Malaysia, Myanmar, Singapore, South Korea, Taiwan, Thailand, and Vietnam. Three categories of country response were identified in regard to national policy support and development. The first category was "None" (Cambodia, Myanmar, and Vietnam) where international partners, driven by humanitarian concerns, lead telehealth activity. The second category was "Proactive" (China, Indonesia, Malaysia, Singapore, South Korea, Taiwan, and Thailand) where national policies were designed with the view that telehealth initiatives are a component of larger development objectives. The third was "Reactive" (Hong Kong and Japan), where policies were only proffered after telehealth activities were sustainable. It is concluded that although complementarity of telehealth policy development is not occurring, increased interjurisdictional telehealth activity, regional clusters, and concerted and coordinated effort amongst researchers, practitioners, and policy makers may alter this trend.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".