Twenty key insights into neighborhood walking experiences of people living with dementia in Metro Vancouver: a quantitative analysis of qualitative data
Bibliographic record
Abstract
With the global rise in dementia prevalence, creating accessible and inclusive neighborhoods is essential to supporting the independence and well-being of people living with dementia. This study examines the neighborhood walking experiences of 26 people living with dementia in Metro Vancouver, British Columbia, focusing on shared experiences and differences across sociodemographic groups. Using a novel matrix framework that quantifies qualitative data, we analyzed insights from sit-down, walk-along, and follow-up interviews, revealing key patterns in walking behaviors and perceptions. Shared priorities, such as route familiarity, safety, and adaptability, emerged alongside distinct subgroup needs. Women valued neighborhood visual appeal more than men, while non-visible minority participants reported higher levels of social interaction compared to visible minority participants. Participants without university education relied more on external wayfinding aids like landmarks, whereas those with higher education used internalized navigation strategies. This innovative quantitative content analysis enables nuanced statistical comparisons across subgroups. Our findings provide actionable insights for urban planning, including universal interventions such as improving sidewalk accessibility and safety, alongside targeted strategies like culturally inclusive spaces, personalized wayfinding aids, and community-based programs. By addressing shared and subgroup-specific needs, this research advances dementia-friendly urban design, public health policies, and global efforts to create equitable and 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".