The experiences of participating in winter among youths with a physical disability compared with their typically developing peers
Bibliographic record
Abstract
BACKGROUND: Having a physical disability and using a wheelchair can create difficulties in navigating the physical and built environment, especially during winter when snow and ice become problematic. Little is known about the experiences of winter among youth who use an assistive mobility device. This study aimed to understand how youth with a physical disability experience winter, compared with typically developing peers. METHODS: A purposive sample of 25 youths (13 with a physical disability; 12 typically developing) completed a 2-week weather journal and photographs in two Canadian cities during winter. These data were used to guide semi-structured interviews with participants. RESULTS: Youths with disabilities experienced many similar challenges in winter, such as health and safety concerns and accessibility issues, compared with typically developing youth - but to a greater extent. Youths with disabilities reported more challenges going outdoors during winter and negative psychosocial impacts, including loneliness and increased dependence, compared with peers without a disability. They also, however, described developing several adaptive strategies to cope with these challenges. CONCLUSIONS: There is a strong need to remove physical and environmental barriers to facilitate the participation and inclusion of youth with disabilities in winter.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".