Implementation and Experiences of Telehealth: Balancing Policies With Practice in Countries of South Asia, Kuwait, and the European Union
Bibliographic record
Abstract
This viewpoint summarizes the discussion that occurred during the "Translating Policy to Practice in Telehealth-Lessons from Global Implementation Experiences" panel that was held virtually at Telemedicon2020, December 18-20, 2020. This panel brought together policy and implementation experts from some countries of South Asia, Kuwait, and the European Union to share their experiences in the development and implementation of telehealth standards and of the scale up of telehealth interventions within health systems. Several common themes arose from the discussion, including the significant role of people; encouragement by respective government policymakers; addressing concerns, particularly related to privacy, confidentiality, and security; and capacity building of human resources. These are discussed in turn, along with the future directions identified by the panelists, which emphasized the need for active encouragement toward the adoption and diffusion of digital health in general and of telehealth in particular. All stakeholders, ranging from governmental policymakers to common citizens, need to come together to build trusting partnerships to realize the advantages offered by telehealth.
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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 | 0.017 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| 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".