Knowledge Translation Strategies Using the Thinking About Epilepsy Program as a Case Study
Bibliographic record
Abstract
In many areas of health promotion and health care there is a need to bring new knowledge from research into practice (knowledge translation). Relevant research alone is not usually sufficient to achieve the ultimate outcome(s) of interest. This study aims to address this gap by outlining practices and outcomes involved in moving research findings into action using the example of the Thinking About Epilepsy program. A case study approach is used to discuss evidence-based principles and steps taken to translate evidence about the Thinking About Epilepsy program into action. Data used to inform this process include organizational documents, observations, and stakeholder interviews. Partnerships and techniques used for knowledge translation are discussed. The process of moving research knowledge into action is discussed explicitly in terms of who the policy makers are, what action is desired, the role of partners, and funding. Using a case study approach the authors have illustrated the importance of starting knowledge translation at the beginning, not at the end, of the research project. The principles discussed in this article can be extended past epilepsy and applied to move research findings relevant to other health conditions, health promotion activities, products, and technologies into action.
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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 | 0.040 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".