THE ROLE OF THE NEIGHBORHOOD-BUILT ENVIRONMENT ON OUTDOOR MOBILITY OF PEOPLE LIVING WITH DEMENTIA
Bibliographic record
Abstract
Abstract Dementia-friendly communities (DFCs) aim to foster a supportive, inclusive and empowering environment that promotes equal rights and resources for people living with dementia and their care partners. Central to DFCs is promoting access and navigation of outdoor spaces and destinations in the neighbourhood. While there is a growing interest and uptake of DFCs in policy and practice, there is scarce empirical evidence on the role of the built environment on mobility and navigation for people living with dementia. The “Dementia-inclusive Spaces for Community Access, Participation, and Engagement (DemSCAPE)” study aims to identify spatial and temporal patterns in activities undertaken outside home by people living with dementia, and ways in which the neighbourhood built environment affects their outdoor mobility and social participation. A series of sit-down and video-and-photo-documented walk-along interviews were conducted with 26 participants who are living with (mild to moderate) dementia or mild cognitive impairment in the Metro Vancouver region of British Columbia, Canada. Findings shed light on how people living with dementia understand and navigate the neighbourhood environment, and features that prompt recall of routes, places, and events, and support orientation and wayfinding. Findings also underscore the importance of participants’ awareness of barriers and obstacles in their neighbourhood, and how they cope with challenges and demands encountered while walking outside. The study offers planners and designers awareness and insights into the lived experience of navigating the neighbourhood environment with the condition of dementia and guidance on adopting a dementia-friendly and inclusive approach in policy and practice.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".