Development of the Standardized Navigation Of Winter Mobility & Accessibility Network (SNOWMAN) course
Bibliographic record
Abstract
Introduction: Manual wheelchairs (MWC) users have limited mobility during winter months as they encounter many environmental barriers that restrict their community participation. This paper outlines the creation and standardization of an outdoor environment designed to simulate the real-life conditions and obstacles experienced by MWC users in winter. Methods and results: This study consisted of four phases. In Phase 1, researchers used a qualitative ethnographic approach to document the specific challenges and adaptive strategies used by MWC users in winter conditions. In Phase 2, key informants with expertise in MWC winter mobility were invited to co-design the Standardized Navigation Of Winter Mobility & Accessibility Network (SNOWMAN) course. Participants reviewed draft design solutions and offered their input and suggestions to expand upon the initial design. A second co-design workshop included additional key informants, including service providers, policymakers, and professionals with expertise in landscape architecture and engineering, to validate the design solution. The workshops resulted in a detailed illustration of the SNOWMAN course, including five sections: platforms with side slopes, a miniature ice rink, curbs and curb cuts, a path with uneven winter surfaces, and modular ramps at various slopes. Phases 3 and 4 marked the conclusion of the study and involved fabrication of the SNOWMAN course and establishment of a standardized protocol for course setup and maintenance. Discussion: The project aimed to offer several additional potential benefits, supported by the various stakeholders across the study phases, that extend beyond creation of a controlled and safe environment for wheelchair users to develop their winter mobility skills. Practicing wheelchair skills in this area may assist wheelchair users in gaining confidence which may ultimately translate to increased participation in the community.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".