Winter Mobility and Community Participation Among People Who Use Mobility Devices: A Scoping Review
Bibliographic record
Abstract
OBJECTIVE: To identify the knowledge, products, and strategies for individuals with mobility-related disabilities used to address challenging winter conditions. DATA SOURCES: AgeLine, OVID, Scopus, and CIHAHL were searched for studies that met the inclusion criteria, from inception to April 2018. Sources for gray literature, or information outside commercial publishing, included ProQUEST, government websites, and manufacturers, vendors, and consumer organization websites. SOURCE SELECTION: Population of people with limited or reduced mobility or mobility device users involved in winter-related environmental conditions; aim was to increase activity, participation, or safety. DATA EXTRACTION: Two reviewers independently applied the inclusion criteria to select eligible sources. Two reviewers independently extracted the data from each source. DATA SYNTHESIS: Twenty-three published peer-reviewed papers were located. Study populations were predominantly those who used wheelchairs (mixed wheelchair type, n=7; power, n=4; manual, n=2), canes (n=3), or specialized winter footwear (n=2). The primary focus of these papers was determined to be tool or device (n=10), recommendations (n=9), strategy (n=2), or resource (n=2). Civic policy documents were variable in citizen responsibility for snow clearing. Limited winter-related supports were identified on consumer organization websites. Although some winter-specific products exist, very few studies have examined the effectiveness of any of these products. CONCLUSIONS: Despite the common experience of challenging winter conditions, a paucity of winter-specific research and innovation relevant for individuals who use mobility devices exists. Researchers, consumers, and industry need to partner to develop novel tools, strategies, resources, and evidence-based recommendations.
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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 | 0.014 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 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".