O3‐07‐06: How CDKTN links knowledge translated research to users
Bibliographic record
Abstract
Currently the number of people with dementia is escalating and health care systems are increasingly challenged resulting in families struggling as caregivers. Additional challenges for care partners include timely access to research-based evidence, practices, services and resources in their communities. The Canadian Dementia Knowledge Translation Network (CDKTN) was established as a network to facilitate effective knowledge translation to improve the health of Canadians with dementia. CDKTN helps researchers, students and practitioners translate their work into practice and distribute it to those who need it most—patients, families and care partners. CDKTN members provide a global vehicle for identifying and resolving gaps between research and practice, prevention, treatment and care. Three areas of focus include: Education and Training in Knowledge Translation—developing specific training programs to bridge knowledge and training gaps, while complementing existing models. Dementia Resource and Knowledge Exchange—targets frontline professionals across a clinical-policy-social spectrum while focusing on knowledge exchange and sharing of resources. Person and Care Partner Centred Knowledge Translation—focuses on improving the quality of life for persons with dementia, their families and care partners. Three main areas of focus for CDKTN's KT efforts: 1) KT training grants: Provides funding opportunities for researchers to conduct KT projects in the area of dementia research. Since the starting the training grants, over 26 have been awarded to develop research into practice. 2) Knowledge broker—CRAKE: Delivers webinars to educate over 6176 individuals about dementia research and resources. 3) KT curriculum development: A focus on trainees (students, researchers and practitioners) in studies and disciplines relevant to dementia. It includes development of specific training programs and opportunities for researchers and health practitioners in knowledge translation and exchange (KT&E) and curriculum development. CDKTN has establish itself as a Canadian-wide KT network devoted to applying dementia research to improve dementia care policies and the quality of care for people with dementia, their families and other care partners.
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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 | 0.023 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.269 | 0.134 |
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".