Analyzing One Health governance and implementation challenges in Mexico
Bibliographic record
Abstract
Establishing a robust One Health (OH) governance is essential for ensuring effective coordination and collaboration among human, animal, and environmental health sectors to prevent and address complex health challenges like zoonoses or antimicrobial resistance. This study conducted a mixed-methods environmental scan to assess to what extent Mexico displays a OH governance and identify opportunities for improvement. Through documentary analysis, the study mapped OH national-level governance elements: infrastructure, multi-level regulations, leadership, multi-coordination mechanisms (MCMs), and financial and OH-trained human resources. Key informant interviews provided insights into enablers, barriers, and recommendations to enhance a OH governance. Findings reveal that Mexico has sector-specific governance elements: institutions, surveillance systems and laboratories, laws, and policies. However, the absence of a OH governmental body poses a challenge. Identified barriers include implementation challenges, non-harmonised legal frameworks, and limited intersectoral information exchange. Enablers include formal and ad hoc MCMs, OH-oriented policies, and educational initiatives. Like other middle-income countries in the region, institutionalising a OH governance in Mexico, may require a OH-specific framework and governing body, infrastructure rearrangements, and policy harmonisation. Strengthening coordination mechanisms, training OH professionals, and ensuring data-sharing surveillance systems are essential steps toward successful implementation, with adequate funding being a relevant factor.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".