Mobility of wheelchair users: a proposed performance assessment framework
Bibliographic record
Abstract
PURPOSE: Few standardized outcome measures used in the clinical assessment of wheelchair user performance assess or document all the factors that influence mobility. This article presents a conceptual framework that suggests a more all-encompassing view of wheelchair mobility performance assessment. METHOD: After determining, through a survey of the literature, which factors influence wheelchair mobility, a conceptual framework was proposed. Outcome measures of wheelchair mobility were then surveyed. An analysis of these assessment tools allowed factors to be determined that were considered in the clinical assessment of wheelchair mobility performance. A comparison of these led to observations being drawn, which could allow the development of a valid and reliable standardized outcome measure to be used in clinics. RESULTS AND CONCLUSION: Future improvements to the clinical assessment of wheelchair mobility performance should focus on developing an assessment tool considering all categories of factors influencing wheelchair mobility. These categories are the user's profile, the wheelchair, the environment, the daily activities and social roles and the assessment and training received. A controlled-environment outcome measure using a standardized obstacle course with fundamental situations would allow rehabilitation therapists to evaluate clients for the selection of a wheelchair and to document their progress during training. This tool should make it possible to extrapolate the results and thereby apply them to daily activities and social roles.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".