KNOWLEDGE MOBILIZATION TO DEVELOP DEMENTIA-INCLUSIVE COMMUNITIES IN THE DEMSCAPE PROJECT
Bibliographic record
Abstract
Abstract Creating a supportive neighborhood built environment that facilitates outdoor mobility, wayfinding, and access to community destinations is a key component in making communities more dementia-inclusive. To help municipalities achieve their vision of dementia-friendly and inclusive streets and outdoor spaces, this Knowledge Mobilization (KM) extension of the Dementia-inclusive Spaces for Community Access, Participation, and Engagement (DemSCAPE) project developed three evidence-based education and training resources for municipal planners, community-based organizations, people with lived experience and care partners. These resources or tools are: 1) “Dementia-Inclusive Planning and Design Guidelines” to inform municipal planners and decision-makers on effective physical planning and design to support people living with dementia; 2) “Neighbourhood Environmental Audit Tool” for community-based dementia advocacy organizations and people with lived experience to conduct audit of the neighbourhood environment and identify intervention projects; and 3) eight-minute documentary video illustrating the lived-experiences of three people living with dementia in urban, suburban and northern areas of British Columbia, Canada. In partnership with the City of Burnaby and the City of Richmond in Metro Vancouver, Canada, three KM activities are being conducted: 1) World Café discussions following screening the short video and an accompanying photo exhibit, 2) focus group discussions with municipal planners to help them apply knowledge of dementia-friendly streets and outdoor spaces to planning and design, and 3) conduct environmental audits of targeted streets in Metro Vancouver engaging people living with dementia and identify actionable built environmental interventions to make their communities more dementia-inclusive.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.032 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".