Constructing a One Health governance architecture: a systematic review and analysis of governance mechanisms for One Health
Bibliographic record
Abstract
The integration of human, animal, and environmental health in the One Health framework is crucial for tackling complex health and environmental issues. Governance structures in One Health initiatives are essential for coordinating efforts, fostering partnerships, and establishing effective policy frameworks. This systematic review, registered with PROSPERO, aims to evaluate governance architectures in One Health initiatives. Searches in PubMed, Scopus, WoS, and Cochrane from 2000 to 2023 were conducted. Key terms focused on peer-reviewed articles, systematic reviews, and relevant grey literature. Nine eligible studies were selected based on inclusion criteria. Data synthesis aimed to assess governance mechanisms' functionality and effectiveness. Among 1277 sources screened, nine studies across diverse regions were eligible. An adapted framework assessed implementation mechanisms of international agreements, categorizing them into Engagement, Coordination, Policies, and Financial domains. The findings highlight the importance of effective governance, stakeholder engagement, and collaborative approaches in addressing One Health's challenges. Identified challenges include deficient intersectoral collaboration, funding constraints, and stakeholder conflicts. Robust governance frameworks are pivotal in One Health paradigms, emphasizing stakeholder engagement and collaboration. These insights guide policymakers, practitioners, and researchers in refining governance structures to enhance human-animal health and environmental sustainability. Acknowledging study limitations, such as methodological variations and limited geographical scope, underscores the importance of further research in this area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".