DEMENTIA WITHOUT BORDERS: BUILDING COMMUNITY CONNECTIONS TO REDUCE STIGMA AND FOSTER INCLUSION
Bibliographic record
Abstract
Abstract BACKGROUND: The concept of social citizenship is gaining traction in the field of dementia studies, but as a practical tool to guide development of supports and services, it remains poorly understood. A one year project to promote collaboration between University of Washington in Seattle and University of British Columbia in Vancouver addressed this very question. Activities were undertaken so these communities could know each other better, with researchers, service providers and people with dementia connecting to share knowledge and expertise. PURPOSE: The project culminated with a public festival to put into practice and share some of what was learned over the year. METHODS: People with dementia and care partners helped plan “Dementia Without Borders”, held at an international park straddling the border between Seattle and Vancouver. 150 people came from the US and Canada, including many people with dementia, family members and friends. The day began with a community walk and gift exchange, followed by a meal and creative activities including poetry readings, music, an art exhibit, and quilt making. RESULTS: Evaluation was overwhelmingly positive with people expressing a sense of hope and belonging. For some, it was their first time to speak openly about having dementia, and meeting others in this space was a joy-filled experience. CONCLUSIONS: This project has leveraged the symbolic power of an international border to raise awareness of the importance of social connection for people with dementia. We further explore how the notion of “dementia without borders” extends theoretical and practical understanding of social citizenship.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.002 | 0.002 |
| 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".