Prioritizing policy issues for knowledge translation: a critical interpretive synthesis
Notice bibliographique
Résumé
BACKGROUND: While calls for promoting evidence-informed policymaking (EIP) have become stronger in recent years, there is a paucity of methods to prioritize issues for knowledge translation (KT) and EIP. As requested by WHO and as part of efforts to address this gap, we conducted a critical interpretive synthesis (CIS) to develop a conceptual framework that outlines the features of priority-setting processes and contextual factors influencing the prioritization of issues for KT efforts. METHODS: We systematically reviewed the literature and used an interpretive analytic approach-the CIS-to synthesize the results and develop the conceptual framework. We used a "compass" question to create a detailed search strategy and conducted electronic searches to identify papers based on their potential relevance to priority-setting for KT efforts and EIP. RESULTS: We identified 161 eligible papers. Our findings on key features of the priority-setting process unpacked three 3 levels of constructs: 'pathways' for identifying and prioritizing policy issues for knowledge translation efforts; 'phases' within each pathway; and 'steps' for each phase. There are three main pathways: (1) explicit and systemic priority-setting processes involving policymakers and stakeholders to determine priority topics (collaborative); (2) a policymaker or stakeholder brings an issue forward or asks for evidence on a particular topic (demand-driven); and (3) a need or policy gap is identified by a knowledge translation platform (supply-driven). Within each pathway, four phases emerged: "Preparatory", "prioritization", "knowledge translation" and "scale-up and sustainability". Across these phases, the following steps were identified: establishing a core team, defining a scope, confirming a timeline, sensitizing stakeholders, generating potential issues, gathering contextual information, setting guiding principles, selecting prioritization criteria, applying the method for prioritization, documenting and communicating priorities, validating and revising priorities, selecting venue for decision-making, implementing priorities, monitoring and evaluation, promoting institutionalization, and engaging in peer learning and exchange of experience. We identified engaging stakeholders and strengthening capacity as cross-cutting elements. Our findings on contextual factors unpacked four categories: (1) institutions; (2) ideas; (3) interests; and (4) external factors. CONCLUSIONS: This CIS generated a multi-level conceptual framework for prioritizing issues for KT efforts and laid the foundation for a WHO tool that supports prioritization in practice. The study contributes meaningfully to both the literature and the operationalization of KT and EIP.
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 | 0,011 | 0,124 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,002 | 0,005 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 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 ».