KNOWLEDGE MOBILIZATION TO CREATE DEMENTIA-INCLUSIVE NEIGHBORHOODS: AN EDUCATIONAL AND AWARENESS-RAISING VIDEO
Bibliographic record
Abstract
Abstract To promote community engagement and increase awareness of dementia-inclusive neighborhood built environment, we have co-created with people with lived experience (PLWE) multiple knowledge mobilization (KM) resources. These resources include an educational and advocacy video and photo exhibit showcasing the project findings to stakeholders and the general public in British Columbia, Canada and beyond. A seven-minute documentary video was produced to illustrate the lived experiences of three people living with dementia in Metro Vancouver and Prince George. This video is one of several public engagement tools for dialogue and challenging stigma surrounding living with dementia in the community that will be mobilized during this phase of the project. This presentation will share the process of community engagement based on the World Café method in two municipalities in Metro Vancouver, Canada. The World Café method encourages conversations, collaborative learning, and generation of new ideas with the video and photo exhibit. This KM project aims to increase awareness and understanding of the features of a dementia-inclusive neighborhood built environment and advocate for positive actions for a broad range of stakeholders, including advocacy groups, community-based organizations, municipalities, and the general public. In partnership with the City of Burnaby and the City of Richmond in Metro Vancouver, these KM activities maximize the impact of the DemSCAPE study’s findings and model various evidence-based approaches to achieving the vision and objectives for developing dementia-inclusive communities.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".