BUILDING INCLUSIVE COMMUNITIES FOR PEOPLE WITH DEMENTIA: TAKING THE LEAD FROM LIVED EXPERIENCE
Bibliographic record
Abstract
Abstract Social citizenship is increasingly adopted in academic circles as a theoretical perspective foregrounding the rights of people with dementia to full participation in their community. However, what this means in practice is poorly understood. Using methods of participatory action research and developmental evaluation, our team conducted a four-year study to learn what social citizenship means from the perspective of people with lived experience, and to put that understanding into action through community programs and services. In phase one, an action group of people with dementia identified stigma as their main concern, and together with the research team co-developed an online toolkit to raise awareness and “flip stigma on its ear”. In phase two, the toolkit was shared with community stakeholders who provided formative feedback, and the team conducted implementation sessions to explore the toolkit’s impact for community programs and services who wanted to be more dementia inclusive. Analysis of interviews with stakeholders and program leaders and field notes from the feedback and implementation sessions identified key themes about how the toolkit has been received and its impact across varying community contexts, e.g. libraries, churches, community centres, and adult day programs: (1) the power of hearing real voices; (2) recognizing stigma; (3) gaining confidence for change; (4) spreading the word; and (5) working with (not for) people with dementia. Overall, these groups found that taking the lead from people with lived experience has been pivotal in helping them take first meaningful steps toward dementia inclusion.
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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 | 0.031 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.015 | 0.013 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.003 | 0.042 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".