Contrasting current challenges from the Brazilian and Canadian national health systems: The Besrour Papers: a series on the state of family medicine in Canada and Brazil.
Bibliographic record
Abstract
OBJECTIVE: To compare the national health systems of Canada and Brazil and how both countries have addressed similar challenges in their primary care sectors. COMPOSITION OF THE COMMITTEE: A subgroup of the Besrour Centre of the College of Family Physicians of Canada developed connections with colleagues in Brazil and collaborated to undertake a between-country comparison, comparing and contrasting various elements of both countries' efforts to strengthen primary care over the past few decades. METHODS: Following a literature review, the authors collectively reflected on their experiences in an attempt to explore the past and current state of family medicine in Canada and Brazil. REPORT: The Brazilian and Canadian primary care systems are faced with similar challenges, including geography, demographic changes, population health inequities, and gaps in universal access to comprehensive primary care services. Although the approaches to addressing these challenges are different in both settings, they highlight the central importance of family physicians in both systems. Both countries continue to face considerable challenges in the context of mental health services in primary care. It remains important for Canada to draw lessons from the primary care systems and reforms of other countries, such as Brazil.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".