Strategies to improve access to physical activity opportunities for people with physical disabilities
Bibliographic record
Abstract
Community-based physical activity opportunities have been shown to help adults with physical disabilities improve their participation in daily activities and reduce social isolation. Despite the known benefits, substantial barriers and challenges inhibit accessibility to these physical activity opportunities. To facilitate the co-construction of strategies to overcome accessibility issues pertaining to community-based physical activity opportunities. In total, 45 individuals with physical disabilities, patients at a rehabilitation hospital, staff members of disability organizations, staff of local or provincial government agencies/departments, kinesiologists, occupational therapists, graduate students, and peer mentors participated in one of four World Cafés held in their respective cities. World Café is a methodology for fostering collaborative, solution-focused conversation that aims to solve problems through collective intelligence. Participants were divided into groups of three to four people and invited to engage in evolving rounds of discussions responding to prompts about accessibility to physical activity in their communities. Transcripts were analyzed using content analysis. In total, 17 strategies were identified, addressing 5 areas: representation and visibility (e.g., prioritize hiring people with a disability), finances (e.g., reduce direct costs for participants), connection and social support (e.g., foster social networks that provide informational support), education and programming (e.g., enhance awareness of existing services and resources), and government programs and policies (e.g., enforce accessibility standards for indoor and outdoor spaces). The findings of this study provide strategies and practical applications for community programs and governments to consider for increasing access to physical activity opportunities for people with physical disabilities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| 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".