KNOWLEDGE TRANSLATION: INTEGRATING AGING RESEARCH AND INNOVATION INTO POLICY AND PRACTICE
Bibliographic record
Abstract
Knowledge translation (KT) is an iterative process for bridging research, policy and practice that can be integrated into research and development to support the relevance and application of research findings. KT includes the mobilization of experiential and scientific knowledge through to the commercialization and application of research innovations. Building KT into research projects is nascent in gerontology, yet articulation of a KT strategy for research impact beyond traditional end-of-grant dissemination is a funding requirement. Integrating KT effectively into research is a complex process that is often misapplied. This symposium will present four cross-disciplinary, Canadian projects that demonstrate critical elements for effective KT in aging research. Grigorovich et al. detail mechanisms of transdisciplinary working, essential for integrated KT, alongside the development and validation of an effectiveness scale used to support transdisciplinary working in research. Demonstrating the first step of any KT initiative, Canham et al. present methods and findings from an applied realist review that explored the current state of the digital divide for older adults. At the other end of the KT process, Fang et al. report on an evaluation of the impact of a knowledge mobilization initiative for aging and technology innovation. Finally, Battersby et al. present findings from an integrated KT project that collaboratively developed, validated, and disseminated national guidelines for conducting mass interinstitutional relocations in long-term care. The symposium will conclude with a discussion led by a KT expert on the implications of using KT to bridge aging research and innovation with policy and practice.
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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 | 0.208 | 0.201 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.016 | 0.073 |
| Scholarly communication | 0.042 | 0.061 |
| Open science | 0.007 | 0.045 |
| Research integrity | 0.018 | 0.018 |
| Insufficient payload (model declined to judge) | 0.010 | 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".