Using participatory research to co‐create the dementia‐inclusive choices for exercise toolkit
Bibliographic record
Abstract
Abstract Background Exercise improves functional abilities of persons living with dementia (PLWD) and provides an opportunity for social engagement, support, and inclusion. Yet, there are few exercise programs available to PLWD, especially in smaller communities. The goal of the Dementia‐Inclusive Choices for Exercise (DICE) project was to use participatory processes to create evidence‐informed knowledge translation (KT) resources to increase the number and quality of exercise opportunities for PLWD. Method The DICE core team includes PLWD, a family care partner (CP), exercise providers (EPs), dementia service providers (DSPs), health care professionals, and researchers. In the early project stages, we confirmed objectives and conducted 1 survey, 2 interviews, 16 focus groups to better understand barriers to and supports needed for exercise among PLWD. Results led to the prioritizing of primary (EPs) and secondary (PLWD & CP) target audiences for the KT resources. Co‐creation of the KT resources followed several steps: 1) drafting content and possible formats for resources over several core team meetings; 2) conducting a one‐day workshop with additional PLWD, CPs, EPs, and DSPs to develop a comprehensive plan for the content and format for each resource, target audience, and dissemination strategies; 3) using iterative design among the core team, resource developers, and additional PLWD, CPs, EPs, and DSPs to develop resources; and 4) usability testing. Result Key information to be translated included understanding the diversity of dementia, rights of PLWD to inclusion, physical activity guidelines/benefits, and dementia‐inclusive communication, practices, and design. This information is delivered through the DICE toolkit, which includes a website, on‐line training modules and manual targeted at exercise providers, destigmatizing videos that show the stories of PLWD, and resources for PLWD and their CPs to increase knowledge and confidence regarding exercise. Subsequent usability testing among EPs, PLWD, and CPs (n=7) identified design and content adjustments. Collaborative and inclusive decision making created improved relationships and ongoing learning between all stakeholders. Conclusion The participatory approach of the DICE project enabled stakeholders to identify and share perspectives and needs throughout the co‐creation process. Working collaboratively and iteratively with a diverse team ensured that the DICE toolkit is relevant and usable to EPs, PLWD, and CPs.
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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 | 0.146 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".