Knowledge Translation Research: The Science of Moving Research Into Policy and Practice
Bibliographic record
Abstract
Research findings will not change health outcomes unless health care organizations, systems, and professionals adopt them in practice. Knowledge translation research is the scientific study of the methods to promote the uptake of research findings by patients, health care providers, managers, and policy makers. Many forms of enquiry addressing different questions are needed to develop the evidence base for knowledge translation. In this paper we will present a description of the broad scope of knowledge translation research with a reflection on activities needed to further develop the science of knowledge translation. Consideration of some of the shared research challenges facing the fields of knowledge translation and continuing professional development will also be presented.
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 | 0.181 | 0.325 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.019 | 0.028 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".