Characterizing how One Health is defined and used within primary research: A scoping review
Notice bibliographique
Résumé
Background and Aim: One Health (OH) approach can be used in multiple ways to tackle a wide range of complex problems, making OH research applications and definitions difficult to summarize. To improve our ability to describe OH research applications, we aimed to characterize (1) the terms used in OH definitions within primary research articles reporting the use of the OH approach, and (2) the who, what, where, when, why, and how (5Ws and H) of the OH primary research articles. Materials and Methods: A scoping review was conducted using nine databases and the search term “One Health” in June 2021. Articles were screened by two reviewers using pre-specified eligibility criteria. The search yielded 11,441 results and screening identified 252 eligible primary research articles. One Health definitions and 5Ws and H data were extracted from these studies. Results: Definitions: One Health was labeled as an “approach” (n = 79) or “concept” (n = 30) that is “multi/cross/inter/trans-disciplinary” (n = 77), “collaborative” (n = 54), “interconnected” (n = 35), applied “locally/regionally/nationally/globally” (n = 84), and includes health pillars (“human” = 124, “animal” = 122, “environmental/ecosystem” = 118). WHEN: Article publication dates began in 2010 and approximately half were published since 2020 (130/252). WHERE: First authors most often had European (n = 101) and North American (n = 70) affiliations, but data collection location was more evenly distributed around the world. WHO: The most common disciplines represented in affiliations were human health/biology (n = 198), animal health/biology (n = 157), food/agriculture (n = 81), and environment/geography (n = 80). WHAT: Infectious disease was the only research topic addressed until 2014 and continued to be the most published overall (n = 171). Antimicrobial resistance was the second most researched area (n = 47) and the diversity of topics increased over time. HOW: Both quantitative and qualitative study designs were reported, with quantitative observational designs being the most common (n = 174). WHY: Objectives indicated that studies were conducted for the benefit of humans (n = 187), animals (n = 130), physical environment (n = 55), social environments (n = 33), and plants (n = 4). Conclusion: This scoping review of primary OH research shows a diverse body of work, with human health being considered most frequently. We encourage continued knowledge synthesis work to monitor these patterns as global issues and the application of OH approaches evolve. Keywords: global One Health research, knowledge synthesis, one health applications, one health definitions.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,012 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,004 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».