Urban Environmental Barriers and Facilities to Mobility and Participation for Older Mobility Device Users
Bibliographic record
Abstract
Abstract Many people use mobility devices to get around. Unfortunately, these mobility device users frequently encounter environmental features and social practices that restrict mobility and social participation. For example, barriers in the built environment can exclude mobility devices users from certain spaces. They also report experiencing discrimination and stigma in the community. However, much of the research in this area has not examined the experiences of older mobility device users in a holistic manner. The purpose of our study was to explore the barriers and facilitators of mobility and participation among people who use wheeled mobility devices. This mixed-methods project used multiple participatory research methods including qualitative interviews, participant-led, community environmental audits, photovoice, mobility tracking using global positioning satellite data and building accessibility audits of participant nominated buildings. We used standardized tools to measure participants’ perceived, physical functioning, anxiety and depression, mobility and mobility device confidence among device users living. The study included 104 participants (64 from the Metro Vancouver and 41 from Quebec City). The primary mobility devices used included manual and power wheelchairs, mobility scooters, canes, crutches and walkers. On average, participants were 58 years of age and 53% were female. Our analysis revealed four main themes: 1) wayfinding challenges; 2) barriers and facilitators in the built environment; 3) the influence of social practices; and 4) temporal and climatic fluctuations. Our findings identified policies and changeable features in the built and social environment that restrict accessibility, which could be remedied by working collaboratively with municipalities and service providers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".