Patterns of community participation across the seasons: A year-long case study of three Canadian wheelchair users
Bibliographic record
Abstract
PURPOSE: The aim of this study was to explore the patterns of wheelchair users' community participation across a one-year period, including periods with substantial differences in weather conditions. We sought to develop a detailed understanding of the patterns of, and influences on, wheelchair use and participation within wheelchair users' own communities. METHOD: We conducted an instrumental case study of three purposefully selected individuals who use a wheelchair. Participants' wheelchairs were instrumented with a GPS data logger and data were collected for one week per month across a year. A prompted recall interview was conducted with participants each month, in order to gain an understanding of the influences on their community participation patterns. RESULTS: For each participant, the percent of trips taken at various trip distance ranges and the mean trips/days and overall distance traveled at or above 0 °C and below 0 °C are reported. Three distinct patterns were identified in response to variations in weather conditions: (1) season and transportation options are linked: winter limits community participation; (2) winter conditions are surmountable: with the right supports year-round participation is maintained; and (3) pre-planning is the key: winter conditions affects ease, choices and options but not overall participation. CONCLUSIONS: While winter weather conditions created community participation challenges, individuals responded differently based on their unique circumstances. The findings highlight the importance of policy that addresses the dynamic nature of weather and the needs of people with disabilities as specific individuals. Implications for rehabilitation Wheelchair users experience both similar and unique challenges regarding seasonal weather conditions that influence their community participation patterns. While some individual wheelchair users effectively maintain their community participation patterns across the year, they employ their own specific strategies in response to winter weather challenges. Ready access to vehicular transportation that is accessible regardless of weather condition is a key factor in promoting community participation across the year for wheelchair users. Accessible community environments can become inaccessible with the addition of winter weather conditions and thus the changing nature of community participation across the seasons should be considered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".