One Health in two countries: The politics of transdisciplinary healthcare collaboration in Canada and the United States
Bibliographic record
Abstract
The One Health perspective highlights the potential synergies between the human, animal, and environmental health sciences, especially in an era of budget shortfalls, climate change, and emerging infectious diseases of zoonotic origin. Canadian physicians and veterinarians arguably lay the foundation of One Health in the late 19 th century, when they pioneered the study of “comparative medicine” in Montreal, but they fell into disciplinary silos before World War I to the lasting detriment of the Canadian population. This article explores both the advantages and impediments to cross-disciplinary healthcare collaboration in Canada, highlighting the country’s vast size, sparse population, and political decentralization in particular, and offers a number of policy recommendations that would allow the country to reclaim its rightful role as a leader in the One Health movement.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.054 | 0.020 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".