ADAPTING STAKEHOLDER WALKABILITY/WHEELABILITY AUDIT TOOL IN NEIGHBORHOOD FOR SENSORY AND COGNITIVE DISABILITIES
Bibliographic record
Abstract
Abstract Neighbourhood accessibility influences health, social inclusion, and overall wellbeing of older adults. It is important to assess neighbourhood accessibility in relation to the diverse needs and challenges brought on by the intersection of aging and disability, particularly sensory and cognitive disabilities. Given the paucity of neighbourhood audit tools tailored for this population, The user-led Stakeholders’ Walkability/Wheelability Audit in Neighbourhoods (SWAN) tool was originally created for people with mobility disabilities and is now being adapted for seniors with sensory and cognitive disabilities to evaluate functionality, safety, appearance, supportive features, and social aspects in their neighbourhoods. In this paper, we present highlights and key takeaways from the process of adapting the SWAN tool for three user groups: people living with 1) Blindness or low vision, 2) Deafness and hearing loss, and 3) Dementia. Key steps in the iterative tool adaptation process include 1) identifying access needs/challenges for the three user groups based on a literature review, 2) online consultation with stakeholders with lived and/or professional experience (N = 4) to prioritize key access needs/challenges that will be captured through the SWAN tool and review draft versions of the tool, and 3) in-person pilot testing of tools with persons with lived experience (N = 2) in two urban/suburban neighbourhoods in British Columbia, Canada. Reflections of team members and input from stakeholders and pilot participants revealed issues that were addressed in tool development, namely 1) length of audit and participant fatigue, 2) legibility of tool, and 3) tailoring audit to participants’ context and needs.
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".