A call for a backward design to knowledge translation
Bibliographic record
Abstract
Despite several calls to support evidence-informed policy-making, variations in uptake of evidence into policy persist. This editorial brings together and builds on previous Knowledge Translation (KT) frameworks and theories to present a simple, yet, holistic approach for promoting evidence-informed policies. The proposed conceptual framework is characterized by its impact-oriented approach and its view of KT as a continuum from the evidence synthesis stage to uptake and evaluation, while highlighting capacity and resource requirement at every step. A practical example is given to guide readers through the different steps of the framework. With a growing interest in strengthening evidence-informed policy-making, there is a need to continuously develop theories to understand and improve the science of KT and its implementation within the field of policy-making.
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 | 0.373 | 0.456 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.009 | 0.063 |
| Scholarly communication | 0.031 | 0.052 |
| Open science | 0.009 | 0.026 |
| Research integrity | 0.016 | 0.029 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".