Improving the Utilization of Research Knowledge in Agri-food Public Health: A Mixed-Method Review of Knowledge Translation and Transfer
Bibliographic record
Abstract
Knowledge translation and transfer (KTT) aims to increase research utilization and ensure that the best available knowledge is used to inform policy and practice. Many frameworks, methods, and terms are used to describe KTT, and the field has largely developed in the health sector over the past decade. There is a need to review key KTT principles and methods in different sectors and evaluate their potential application in agri-food public health. We conducted a structured mixed-method review of the KTT literature. From 827 citations identified in a comprehensive search, we characterized 160 relevant review articles, case studies, and reports. A thematic analysis was conducted on a prioritized and representative subset of 33 articles to identify key principles and characteristics for ensuring effective KTT. The review steps were conducted by two or more independent reviewers using structured and pretested forms. We identified five key principles for effective KTT that were described within two contexts: to improve research utilization in general and to inform policy-making. To ensure general research uptake, there is a need for the following: (1) relevant and credible research; (2) ongoing interactions between researchers and end-users; (3) organizational support and culture; and (4) monitoring and evaluation. To inform policy-making, (5) researchers must also address the multiple and competing contextual factors of the policy-making process. We also describe 23 recommended and promising KTT methods, including six synthesis (e.g., systematic reviews, mixed-method reviews, and rapid reviews); nine dissemination (e.g., evidence summaries, social media, and policy briefs); and eight exchange methods (e.g., communities of practice, knowledge brokering, and policy dialogues). A brief description, contextual example, and key references are provided for each method. We recommend a wider endorsement of KTT principles and methods in agri-food public health, but there are also important gaps and challenges that should be addressed in the future.
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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 | 0.263 | 0.466 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.051 | 0.044 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".