Developing a Knowledge Translation Strategy for a Centre of Childhood Disability Research: Description of the Process
Bibliographic record
Abstract
Knowledge translation (KT) is a topic of interest for researchers; however, little has been published about how to plan and prioritize KT activities.is article describes the development and outcomes of a KT strategic planning activity for a research organization.An online survey and planning meeting resulted in the identification of six priority areas: engaging families, nurturing partnerships, optimizing access to knowledge, KT capacity building, advancing KT science, and funding for future KT activities.e organization collectively determined short-and long-term objectives, strategies, and measurable outcomes for the KT priority areas.e strategic planning process helped with prioritizing KT activities and engaged members in a collaborative discussion of mutual interest.e process described may be useful for others interested in developing KT strategic plans.
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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 | 0.125 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".