Telehealth in the Circumpolar North: A Perspective of Access and Connectivity
Bibliographic record
Abstract
Telehealth deployments enable access to a distant healthcare provider remotely through communication channels. Telehealth typically connects two endpoints, such as a patient site and a healthcare provider, often using end-user devices such as laptops and mobile devices connected to the Internet. Telehealth aims to reduce healthcare inequity for patients from remote locations lacking critical healthcare facilities. However, limited Internet access and the lack of computational resources in those regions prevent patients from utilizing telehealth effectively. The circumpolar north, consisting of Arctic regions such as Alaska, Northern Canada, Greenland, and Svalbard, has unstable or expensive access to the Internet despite being part of developed, well-connected countries. The distance from their country’s economic hubs and these territories’ vastness, coupled with their minimal population, pose challenges for telehealth and Internet access. This paper identifies the network, execution, and process challenges for equitable telehealth access and connectivity in the circumpolar north. We survey the state-of-the-art for potential solutions and research optimizations. We conclude the paper by discussing the evolving landscape in providing telehealth access to these remote communities.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".