Strengthening the One Health Approach in the Eastern Mediterranean Region
Bibliographic record
Abstract
One Health aims to use a multidisciplinary approach to combat health threats at animal, human, and environmental health interfaces. Among its broad focus areas are issues related to food safety, the control of zoonoses, laboratory services, neglected tropical diseases, environmental health, biosafety and biosecurity, and combatting antimicrobial resistance. A roundtable session was conducted on November 18, 2021, as part of the Eastern Mediterranean Public Health Network's (EMPHNET) seventh regional conference to highlight what role Global Health Development (GHD)|EMPHNET can play to strengthen the One Health approach. This viewpoint summarizes the findings of the roundtable discussion to highlight the experts' viewpoints on strengthening the One Health approach, including the extent of zoonotic diseases and the dynamics of pathogens and emerging diseases; the occurrence of antimicrobial-resistant pathogens as a silent pandemic; issues surrounding the globalization of trade and food safety; the importance of integrated solutions as a new norm; issues around the institutionalization and governance toward effective operationalization of the One Health approach in the region; and how the One Health approach can be operationalized at global, regional, and local levels. The panel concluded that One Health is an integrated unifying approach that aims to sustainably balance and optimize the health of people, animals, and ecosystems, and provided recommendations to strengthen the One Health approach. It also discussed how GHD|EMPHNET can play its role in transferring the concept of One Health from theory to practice via a solid operationalization road map guide at the Eastern Mediterranean region level. The five broad priority areas of this operational guide include (1) establishing and strengthening a governance architecture, legal framework, and policy and advocacy structure for One Health operationalization in the region; (2) fostering coordination, communication, and collaboration for One Health actions across the region and beyond; (3) building the workforce capacity for effective One Health operationalization in the region; (4) supporting regional platforms for timely, effective, and efficient data sharing and exchange on all One Health-related issues; and (5) supporting risk communication, behavior change communication, and community engagement efforts in the region.
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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 | 0.023 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".