THE DREAM TOOLKIT: CO-DEVELOPED TRAINING AND RESOURCES TO PROMOTE WELL-BEING OF PERSONS WITH DEMENTIA
Bibliographic record
Abstract
Abstract Promoting wellbeing of persons with dementia is a priority. Engaging multi-perspective partners to co-develop interventions creates impactful solutions. We will describe the process, output, and lessons from the Dementia Resources for Eating, Activity, and Meaningful inclusion (DREAM) project, which co-developed tools/resources with persons with dementia, care partners, community service providers, health care professionals, and researchers. We aimed to increase supports for physical activity, healthy eating, and wellbeing of persons with dementia. Our process included: 1) Engaging/maintaining the DREAM Steering Team; 2) Setting/navigating ways of engagement; 3) Prioritizing audience and content of the toolkit; 4) Drafting content & format of toolkit; 5) Iterative co-development of tools/resources; 6) Usability testing; 7) Implementation and evaluation. In virtual meetings, the DREAM Steering Team confirmed toolkit audiences (primary: community service providers; secondary: persons with dementia and care partners) and discussed and evolved content areas. An environmental scan identified existing, high-quality resources aligned with content areas. The DREAM Steering Team alongside additional community partners and external contractors iteratively co-developed new resources/tools to meet gaps. The DREAM toolkit includes a website, seven learning modules about dementia, healthy eating, and physical activity, a learning manual, six videos, nine handouts, and four wallet cards (www.dementiawellness.ca). Co-development participants rated the virtual co-development process highly in relation to the principles and enablers of Authentic Partnership in a process evaluation. Through the co-developed DREAM toolkit, we anticipate community service providers will learn to provide inclusive wellness programs and services to benefit persons with dementia and their families.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".