Dementia resources for eating, activity, and meaningful inclusion (DREAM) toolkit co-development: process, output, and lessons learned
Bibliographic record
Abstract
Promoting wellbeing of persons with dementia and their families is a priority of research and practice. Engaging diverse partners, including persons with dementia and their families, to co-develop interventions promotes relevant and impactful solutions. We describe the process, output, and lessons learned 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 with the aim of increasing supports for physical activity, healthy eating, and wellbeing of persons with dementia. Our process included: (1) Engaging and maintaining the DREAM Steering Team; (2) Setting and navigating ways of engagement; (3) Selecting the priority audience and content; (4) Drafting the toolkit; (5) Iterative co-development of tools and resources; (6) Usability testing; and (7) Implementation and evaluation. In virtual meetings, the DREAM Steering Team confirmed the toolkit audiences (primary: community service providers; secondary: persons with dementia and care partners) and identified and evolved content areas. An environmental scan identified few existing, high-quality resources aligned with content areas. The Steering Team, additional multi-perspective partners, and external contractors iteratively co-developed new tools/resources to meet gaps over a 4-month virtual process that included virtual meetings, email exchange of documents and feedback, and one-on-one calls by telephone or email. The final DREAM toolkit includes a website with seven learning modules (on the diversity of dementia, rights and inclusion of persons living with dementia, physical activity, healthy eating, dementia-inclusive practices), a learning manual, six videos, nine handouts, and four wallet cards ( www.dementiawellness.ca ). Our co-development participants rated the process highly in relation to the principles and enablers of authentic partnership even though all engagement was virtual. Through use of the co-developed DREAM toolkit, we anticipate community service providers will gain the knowledge and confidence needed to provide dementia-inclusive wellness programs and services that 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 | 0.099 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".