Developing a Knowledge Translation (KT) 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. This 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. The organization collectively determined short- and long-term objectives, strategies, and measurable outcomes for the KT priority areas. The strategic planning process helped with prioritizing KT activities and engaged members in a collaborative discussion of mutual interest. The process described may be useful for others interested in developing KT strategic plans Résumé: L’application des connaissances (AC) est devenu une topique d’intérêt pour les chercheurs, cependant peu a été publié sur la façon de planifier et de prioriser les activités d’AC. Ce rapport décrire la développement et les résultats d’une activité de planification stratégique d’AC pour une organisation de recherche. Un sondage en direct et les réunions de planification ont résulté dans l’identification des six domaines prioritaires : engagement des familles, entretenir des partenariats, optimisation l’accès aux connaissances, renforcements des capacités d’AC, avancement de la science d’AC, et financement pour les activités d’AC dans la future. L’organisation a déterminé, collectivement, les objectifs à court et à long terme, les stratégies et les résultats mesurables pour les domaines prioritaires susmentionnés. La procédure de planification stratégique ont aidé avec la priorisation des activités d’AC et a engagé des membres dans une discussion collaborative de l’intérêt mutuel. Le processus décrit peut être utile pour d’autres groupes intéressés dans le développement des plans stratégiques d’AC.
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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 | 0.056 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".