Initial validation and feasibility of a Standardized Navigation Of Winter Mobility & Accessibility Network (SNOWMAN) course for wheelchairs
Bibliographic record
Abstract
Many Canadian manual wheelchair users face many challenges in winter months such as slippage on ice- or snow-covered ramps, snow windrows, and casters becoming stuck in deep or hard packed snow. These barriers impact wheelchair users' ability to participate in the community. This study aimed to evaluate the validity and feasibility of a winter wheelchair obstacle course known as the Standardized Navigation Of Winter Mobility & Accessibility Network (SNOWMAN). The results demonstrated that the SNOWMAN course authentically represented real-world winter conditions, as confirmed by participant responses and qualitative feedback from four manual wheelchair users. The course was comprehensive, covering a range of winter obstacles typically encountered by wheelchair users. Construct validity was established by differentiating performance between manual wheelchairs and a motorized platform with snow tracks, showcasing varying completion times and device satisfaction levels. Feasibility was also assessed, with the administration protocol being mostly adhered to, safety measures implemented, and usability scores meeting acceptable thresholds. The SNOWMAN course showed promise for evaluating wheelchair adaptations and devices for winter conditions, as well as training users in winter mobility skills. Future research directions include comparing different wheeled mobility devices, exploring adaptations for usability in winter, and developing new technology tailored for challenging terrains and winter conditions. The SNOWMAN course could serve as a valuable tool for both research and clinical applications in enhancing winter mobility for wheelchair users.
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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".