STARTING FROM STRENGTH: BUILDING PARTNERSHIPS IN COMMUNITY ENGAGED INTERVENTION RESEARCH FOR DEMENTIA INCLUSION
Bibliographic record
Abstract
Abstract The majority of people with dementia live at home, often disconnected and isolated from their community. Canada’s Public Health Agency recognizes that community-based organizations are well positioned to address this issue, and through its Dementia Community Investment is advancing social innovation as a strategy for increasing inclusion, well-being, and quality of life for this growing population. The Building Capacity Project (2019-23) used asset-based community development to foster dementia inclusion through community programs and activities. Using developmental evaluation with community partners in Vancouver Canada, we identified a core set of guiding principles and promising practices for supporting dementia inclusive innovations in community programming: addressing stigma; including lived experience; organizational coaching; and social networking. Now in Phase 2 (2023-25), we are doing community-engaged intervention research, using the Evidence Based System for Innovation Support as a theoretical framework to scale out impacts with new partners in urban and rural communities across the region. As a first step, we conducted semi-structured interviews with 12 representatives from seven community organizations. Based on a thematic analysis of the interview data, we identified three areas for further capacity building for dementia inclusion: (1) reframing dementia as a shared experience; (2) growing confidence; (3) centering joy; and (4) making connections. These findings set the stage for further work as organizations identify strategies for reaching out to people with lived experience at the local level, expand opportunities for more diverse program offerings in multiple languages, and leverage opportunities for integration across municipalities, health systems, and the social services sector.
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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 | 0.188 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.006 | 0.039 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".