Low Earth Orbit Communication Satellites: A Positively Disruptive Technology That Could Change the Delivery of Health Care in Rural and Northern Canada
Bibliographic record
Abstract
Canada is a progressive nation that endeavors to provide comprehensive, universal, and portable health care to all its citizens. This is a challenge for a country with a population of 40 million living within a land expanse of 10 million km2 and where 18% live in rural or highly remote locations. The combined population of Yukon, Northwest Territories, and Nunavut is only 128,959 (0.32% of the population), living within 3.92 million km2, and many of these citizens live in isolated communities with unique health needs and social issues. The current solution to providing health care in the most remote locations has been to transport the patient to the health care provider or vice versa, which incurs considerable financial strain on our health care system and personal stress to the patient and provider. The recent global deployment of low Earth orbit communication satellites (LEO-ComSats) will change the practice and availability of online medicine everywhere, especially in northern Canada. The deployment of LEO-ComSats could result in disruptive but positive changes in medical care for underserved communities in remote geographic locations across Canada. LEO-ComSats can be used to demonstrate online medical encounters between a patient and a doctor in Canada, separated by thousands of kilometers. Most certainly, the academic medical centers in lower Canada could perform online telementored medical care to our northern communities like the remote care provided to many Canadians during the COVID-19 pandemic. An online health care model requires effective design, testing, and validation of the policies, standards, requirements, procedures, and protocols. Although the COVID-19 pandemic was the initial prime mover across all of Canada in the use of online medical encounters and creating rapidly devised reimbursement models, it was nonetheless created reactively, using real-time managerial fiat and poorly defined procedures based on minimal pedagogical experience, which made it "difficult to prove it was universally safe." It is essential to proactively derive the medical policies, standards, and procedures for telementored medicine and "prove it is safe" before LEO-ComSat technology is ubiquitously deployed in northern Canada. This viewpoint was written by subject matter experts who have researched online and internet-based medicine for many years, sometimes 3 decades. In many cases, a literature review was not necessary since they already had the articles in the bibliography or knowledge in their possession. In many cases, internet search engines (ie, Google or PubMed) and Canadian government documents were used to provide corroborating evidence.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".