Discriminatory validity of the Aspects of Wheelchair Mobility Test as demonstrated by a comparison of four wheelchair types designed for use in low-resource areas
Bibliographic record
Abstract
BACKGROUND: Comparative effectiveness research on wheelchairs available in low-resource areas is needed to enable effective use of limited funds. Mobility on commonly encountered rolling environments is a key aspect of function. High variation in capacity among wheelchair users can mask changes in mobility because of wheelchair design. A repeated measures protocol in which the participants use one type of wheelchair and then another minimises the impact of individual variation. OBJECTIVES: The Aspects of Wheelchair Mobility Test (AWMT) was designed to be used in repeated measures studies in low-resource areas. It measures the impact of different wheelchair types on physical performance in commonly encountered rolling environments and provides an opportunity for qualitative and quantitative participant response. This study sought to confirm the ability of the AWMT to discern differences in mobility because of wheelchair design. METHOD: Participants were wheelchair users at a boarding school for students with disabilities in a low-resource area. Each participant completed timed tests on measured tracks on rough and smooth surfaces, in tight spaces and over curbs. Four types of wheelchairs designed for use in low-resource areas were included. RESULTS: The protocol demonstrated the ability to discriminate changes in mobility of individuals because of wheelchair type. CONCLUSION: Comparative effectiveness studies with this protocol can enable beneficial change. This is illustrated by design alterations by wheelchair manufacturers in response to results.
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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 | 0.024 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".