Analyse comparative des initiatives One Health en Guinée et en République Démocratique du Congo : Un appel à l’opérationnalisation/ Comparative analysis of One Health initiatives in Guinea and the Democratic Republic of Congo: A call for operationalization
Bibliographic record
Abstract
La Guinée et la République Démocratique du Congo (RDC) sont deux pays confrontés à des maladies zoonotiques (ré)émergentes, lesquelles représentent de graves menaces pour la santé publique et pour l’économie. Cela renforce l’importance de mettre l'accent sur les approches interdisciplinaires pour la prévention, la détection et l’atténuation des maladies infectieuses afin de mettre en place des systèmes de réponses adéquats. Dans les dernières années, des efforts ont été fournis dans les deux pays pour la conception, la mise en œuvre et la promotion de l’approche “Une Seule Santé” (One Health) qui offre des solutions à l’interface homme-animal-plante-écosystèmes. Cependant, dans ces pays, il n’existe pas une approche systémique “Une Seule Santé” qui soit réellement opérationnelle. Ainsi, cet article vise à faire une analyse comparative des initiatives « One Health » (OH) en Guinée et en RDC. Les résultats suggèrent qu'il existe un engagement fort de la part du gouvernement guinéen à signer un ordre conjoint de collaboration entre les trois départements clés, mais la coopération et la collaboration entre les différents secteurs et disciplines font défaut. En RDC, trois plateformes existent, mais leurs actions ne sont pas coordonnées, ce qui démontre les lacunes dans la vision globale que devrait avoir l’approche OH. Le défi majeur dans ces deux pays est d'adopter une approche holistique pour dépasser les structures et les paradigmes organisationnels et disciplinaires pour développer une véritable coopération entre tous les secteurs directement ou indirectement touchés par les maladies à potentiel épidémique. Guinea and the Democratic Republic of Congo (DRC) are two countries facing (re)emerging zoonotic diseases, which pose serious threats to public health and the economy. This reinforces the importance of emphasizing interdisciplinary approaches for the prevention, detection, and mitigation of infectious diseases to put in place adequate response systems. In recent years, efforts have been made in both countries for the design, implementation, and promotion of the “One Health” (OH) approach which offers solutions at the human-animal-animal-plant-ecosystems interface. However, in these countries, there is no operational OH systemic approach. Thus, this article aims to make a comparative analysis of the OH initiatives in Guinea and the DRC. Findings suggest there is a strong commitment on the part of the government of Guinea to sign a joint order of collaboration between the three key departments, but cooperation and collaboration between different sectors and disciplines is lacking. In the DRC, three platforms exist but are not coordinated, which shows gaps in the overall vision that OH should be in the country. The major challenge in these two countries is to adopt a holistic approach to go beyond organizational and disciplinary structures and paradigms to develop real coordination and cooperation between all the sectors directly or indirectly affected by diseases with epidemic potential.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".