The application of knowledge synthesis methods in agri-food public health: Recent advancements, challenges and opportunities
Notice bibliographique
Résumé
Knowledge synthesis refers to the integration of findings from individual research studies on a given topic or question into the global knowledge base. The application of knowledge synthesis methods, particularly systematic reviews and meta-analysis, has increased considerably in the agri-food public health sector over the past decade and this trend is expected to continue. The objectives of our review were: (1) to describe the most promising knowledge synthesis methods and their applicability in agri-food public health, and (2) to summarize the recent advancements, challenges, and opportunities in the use of systematic review and meta-analysis methods in this sector. We performed a structured review of knowledge synthesis literature from various disciplines to address the first objective, and used comprehensive insights and experiences in applying these methods in the agri-food public health sector to inform the second objective. We describe five knowledge synthesis methods that can be used to address various agri-food public health questions or topics under different conditions and contexts. Scoping reviews describe the main characteristics and knowledge gaps in a broad research field and can be used to evaluate opportunities for prioritizing focused questions for related systematic reviews. Structured rapid reviews are streamlined systematic reviews conducted within a short timeframe to inform urgent decision-making. Mixed-method and qualitative reviews synthesize diverse sources of contextual knowledge (e.g. socio-cognitive, economic, and feasibility considerations). Systematic reviews are a structured and transparent method used to summarize and synthesize literature on a clearly-defined question, and meta-analysis is the statistical combination of data from multiple individual studies. We briefly describe and discuss key advancements in the use of systematic reviews and meta-analysis, including: risk-of-bias assessments; an overall quality-of-evidence approach; engagement of stakeholders; Bayesian, multivariate, and network meta-analysis; and synthesis of diagnostic test accuracy studies. We also highlight several challenges and opportunities in the conduct of systematic reviews (e.g. inclusion of grey literature, minimizing language bias, and optimizing search strategies) and meta-analysis (e.g. inclusion of observational studies and approaches to address the insufficient reporting of data and significant heterogeneity). Many of these developments have yet to be comprehensively applied and evaluated in an agri-food public health context, and more research is needed in this area. There is a need to strengthen knowledge synthesis capacity and infrastructure at the regional, national, and international levels in this sector to ensure that the best available knowledge is used to inform future decision-making about agri-food public health issues.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,178 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,010 | 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 | 0,002 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».