Comparative Analysis of One Health Policies in Asia for Exploring Opportunities for British Columbia in Canada
Bibliographic record
Abstract
In response to emerging challenges that intersect humans, animals, and environments, there is growing international exigent need to adopt 'One Health' approaches. While One Health efforts are emerging in British Columbia in Canada, there are still challenges to overcome in the adoption of a One Health approach in policymaking. We conducted a comparative analysis of One Health policies in Asia, specifically, Singapore, Hong Kong, Bangladesh, and Thailand, which have well-established and sophisticated One Health approaches, to determine good practices in the implementation of One Health that could be considered for adoption in British Columbia. We conducted a literature review and scan of public-facing One Health websites, strategic action plans, and health databases, complemented by 13 semi-structured interviews with researchers, educators, service providers, human and animal health experts, and policymakers in our chosen Asian jurisdictions and British Columbia. While there was diversity in the One Health approaches taken by four jurisdictions, three key characteristics were present in policymaking processes in all of them: a national One Health strategic action plan, inter-ministerial coordination, and flexibility in the working relationships of public servants. One Health presents an opportunity for British Columbia to take a novel approach to public health policymaking, the one that is more holistic and effective at addressing shared health challenges.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".