EXPLORING FACTORS INFLUENCING WALKING ACTIVITY IN PEOPLE WITH DEMENTIA: A SPATIAL ANALYSIS IN METRO VANCOUVER
Bibliographic record
Abstract
Abstract As the number of people living with dementia (PLWD) increases in Canada, the significance of the neighborhood built environment grows, as most PLWD live at home rather than in long-term care facilities. This study investigates how built environment variables predict the overall outdoor walking activity of PLWD in Metro Vancouver. Twenty-five participants living with dementia from various cities in Metro Vancouver participated. We used exploratory factor analysis and multiple linear regression to analyze the relationship between built environment factors and outdoor walking activity. Our results revealed three significant factors: “Macro environment: accessibility to public transportation and street network,” “Micro environment: Pedestrian-oriented design,” and “General characteristics: Mixed land use and sidewalk suitability,” constructed from 14 built environment variables. These factors significantly influenced the outdoor walking activity of PLWD. The multiple linear regression results indicated that “Macro environment – accessibility to public transportation and street network” had a substantial positive effect (p =.007, β = 0.469), “Micro-environment: Pedestrian-oriented design” showed a moderately positive impact (p =.065, β = 0.305), and “General characteristics: mixed land use and sidewalk suitability” exhibited a significant positive influence (p =.015, β = 0.414) on outdoor walking activity. Our findings provide valuable insights for policymakers, urban planners, designers, landscape designers, and transportation planners, emphasizing the crucial collaboration needed to develop dementia-friendly neighborhood plans that cater to the diverse needs of individuals living with dementia, ensuring safe and accessible environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".