Knowledge translation: translating research into policy and practice
Bibliographic record
Abstract
OBJECTIVE: This paper provides a theoretical-reflective study of knowledge translation concepts and their implementation processes for using research evidence in policy and practice. RESULTS: The process of translating research into practice is iterative and dynamic, with fluid boundaries between knowledge creation and action development. Knowledge translation focuses on co-creating knowledge with stakeholders and sharing that knowledge to ensure uptake of relevant research to facilitate informed decisions and changes in policy, practice, and health services delivery. In Brazil, many challenges exist in implementing knowledge translation: lack of awareness, lack of partnerships between researchers and knowledge-users, and low research budgets. CONCLUSIONS: An emphasis on knowledge translation has the potential to positively impact health outcomes. Future research in Brazil is needed to study approaches to improve the uptake of research results in the Brazilian context.
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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 | 0.220 | 0.301 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.007 | 0.054 |
| Scholarly communication | 0.026 | 0.030 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".