Evaluating a new mobility device: feedback from women with disabilities in India
Bibliographic record
Abstract
PURPOSE: To gather the opinions of potential wheeled mobility device users at an early stage in the design process to ensure the development of technology which would meet their functional needs. METHOD: Eight women with bilateral lower extremity disabilities living in Gujarat state, India, participated in this study. The women were introduced to a working model of a new wheeled ground mobility device (GADI2) for a brief trial and participated in a feedback interview which solicited information on different aspects of the design, specifically the interface between the device and the user, the physical environment and the sociocultural environment. Both qualitative and quantitative data were collected and analysed. RESULT: Although the overall response to the device was positive, there was a lack of consensus in some of the feedback gathered. There were varying opinions across the participants and recommendations were often in opposition to what would typically be recommended in a traditional rehabilitation setting. CONCLUSIONS: This study investigates and discusses the research findings from a rehabilitation perspective with a focus on the functional versus technical design aspects. The importance of involving potential consumers in the design of technology is highlighted. The small sample size and lack of consensus in some of the results indicates the need for further research and field testing of this new mobility device design.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".