Influence of the built environment on community mobility of people living with visual disabilities: a scoping review
Bibliographic record
Abstract
Understanding how the outdoor environment shapes the community mobility of people with visual disabilities is key to designing an accessible public realm and facilitating their rights to use outdoor spaces. A scoping review was conducted to explore 1) What aspects of the built environment affect the community mobility of persons with visual disabilities? and 2) How does the built environment affect the community mobility of persons with visual disabilities? Forty-three peer-reviewed publications from 2000 to 2022 were included after conducting database searches, screening of articles, and data charting. Studies focused on micro-environmental features related to sidewalks and crosswalks (e.g. landmarks, curbs, curb ramps, tactile warning/guiding surfaces, and accessible pedestrian signals), and broad environmental factors (e.g. neighbourhood amenities and street layout) and their influence on orientation, wayfinding, and safety. The paper discusses the role of the built environment in 1) posing barriers to outdoor mobility (e.g. potholes, poorly designed curb cuts, obstacles at waist-height or eye-level, poor lighting, inadequate pedestrian signal, complicated street layout), and 2) offering cues (e.g. visual, tactile, auditory, olfactory, kinaesthetic) for spatial perception and navigation. Focusing on how the built environment shapes community mobility is necessary to enhance accessibility through urban planning and design and assistive technology.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".