Knowledge mobilization for and with people with lived experience of dementia
Bibliographic record
Abstract
BACKGROUND: Engaging people with lived experience of dementia (i.e., people living with dementia, family/friend care partners) in all phases of research contributes to more relevant and meaningful research findings. Mobilizing knowledge to diverse audiences is critical to getting scientific findings into the hands of knowledge users, including people with lived experience of dementia. To ensure the goals of patient engagement and knowledge mobilization (KM) were achieved, two cross-cutting programs within the Canadian Consortium on Neurodegeneration in Aging (CCNA) were established: a KM program to support KM activities and a lived experience program, Engagement of People with Lived Experience of Dementia (EPLED). The EPLED Advisory Group consists of people with lived experience of dementia, to support the engagement of people with lived experience in research. METHOD: In addition to their respective core activities, the KM and EPLED programs united to work collaboratively to achieve shared goals of increasing the involvement of people with lived experience throughout the CCNA network, including in KM. Together, the programs undertook activities to raise the profile of people with lived experience of dementia, involving EPLED in KM activities as well as planning and decision making. Over time, this led to a culture shift within CCNA where researchers increasingly sought out and involved the perspectives of people with lived experience in research and KM activities. RESULT: People with lived experience of dementia are now key collaborators at all stages of scientific research within CCNA, including in planning and executing KM activities. Evaluation data indicate these activities are valued by researcher, trainee and public audiences. CONCLUSION: The integration of people with lived experience of dementia within CCNA's research and KM projects has been a resounding success. EPLED and KM will continue to collaborate on integrating people with lived experience in CCNA's research activities, and support researchers in developing KM activities and skills.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".