Enhancing Urban Accessibility: Reliability and Validity Assessment of the Stakeholders’ Walkability/Wheelability Audit in Neighbourhoods Tool
Bibliographic record
Abstract
As Canada’s population ages and disability prevalence increases, understanding the built environment’s impact on mobility and social participation is essential. This study evaluates the measurement properties of the Stakeholders’ Walkability/Wheelability Audit in Neighbourhoods (SWAN) tool, a user-led instrument designed to assess environmental factors affecting older adults and individuals with disabilities. Using community-based participatory research, we recruited 54 participants from five cities to assess the SWAN tool’s inter-rater reliability, construct validity, and internal consistency. The results indicated a high overall inter-rater reliability of 85.22%, with substantial Cohen’s Kappa coefficients across domains, particularly in the Safety domain (0.73). The construct validity was confirmed through moderate to strong correlations with established measures, notably a correlation of 0.79 between the Street Crossing subdomain and the Sidewalk Index. The internal consistency analysis showed excellent reliability in the Functionality domain (α = 0.95) and a lower consistency value in the Social Environment domain (α = 0.63), suggesting the need for further refinement. These findings provide preliminary evidence of the SWAN tool’s potential for evaluating neighbourhood accessibility. By identifying barriers and facilitators to mobility, the SWAN tool can guide urban planning efforts aimed at creating inclusive environments for aging populations and individuals with disabilities. Future research should focus on larger samples to explore structural validity. Ultimately, the SWAN tool can contribute to improving the quality of life of vulnerable populations and promote more equitable urban policy development.
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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 | 0.042 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".