Telemedicine in the driver's seat: new role for primary care access in Brazil and Canada: The Besrour Papers: a series on the state of family medicine in Canada and Brazil.
Bibliographic record
Abstract
OBJECTIVE: To contrast how Brazil's and Canada's different jurisdictional and judicial realities have led to different types of telemedicine and how further scale and improvement can be achieved. COMPOSITION OF THE COMMITTEE: A subgroup of the Besrour Centre of the College of Family Physicians of Canada and Canadian telemedicine experts developed connections with colleagues in Porto Alegre, Brazil, and collaborated to undertake a between-country comparison of their respective telemedicine programs. METHODS: Following a literature review, the authors collectively reflected on their experiences in an attempt to explore the past and current state of telemedicine in Canada and Brazil. REPORT: Both Brazil and Canada share expansive geographies, creating substantial barriers to health for rural patients. Telemedicine is an important part of a universal health system. Both countries have achieved telemedicine programs that have scaled up across large regions and are showing important effects on health care costs and outcomes. However, each system is unique in design and implementation and faces unique challenges for further scale and improvement. Addressing regional differences, the normalization of telemedicine, and potential alignment of telemedicine and artificial intelligence technologies for health care are seen as promising approaches to scaling up and improving telemedicine in both countries.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".