OLDER ADULTS WITH DISABILITIES AS CORESEARCHERS IN COMMUNITY-ENGAGED RESEARCH ON MOBILITY AND ACCESS
Bibliographic record
Abstract
Abstract Neighbourhood access and participation opportunity significantly impacts the health, social inclusion, and overall wellbeing of older adults especially those with mobility, sensory and cognitive disabilities. Stakeholders Walkability/Wheelability Audit in Neighbourhoods (SWAN) is a community engaged mixed method project where user-led audits are conducted in neighbourhoods complemented by semi-structured interviews to evaluate the role of built environment on mobility, access and participation of older adults and people with disabilities across five cities within Metro Vancouver, Canada. Using a community engaged lens starting from initial engagement to more advanced collaboration, this project involves various community stakeholders in different capacities fostering a two-way exchange of information between researchers and community members. Study participants as coresearchers participate in research tool adaptation, data collection, analysis and knowledge mobilization. Additionally, city staff members partner with the research team to identify key areas in cities for data collection, collaborate in knowledge mobilization activities and the development of complementary implementation projects. The SWAN project is part of a larger partnership project titled, “Mobility, Access and Participation” (MAP). Preliminary findings from this project underscores neighbourhood accessibility as it relates to functionality, safety, appearance, supportive features and social engagement opportunities for older adults with disabilities while highlighting some distinct challenges across different types of disabilities. Insights from the SWAN project has the potential inform both academic scholarship and local government policy around mobility, access and social. Findings underscores the importance of community engaged research in informing programmatic and policy changes using a social equity lens.
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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 | 0.026 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".