Prioritizing policy issues for knowledge translation: a critical interpretive synthesis
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.535 | 0.719 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.077 | 0.052 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.031 | 0.032 |
| Open science | 0.011 | 0.019 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".