DEVELOPMENT OF THE USABILITY SCALE FOR POWER WHEELCHAIRS AND ITS APPLICATION IN POWER WHEELCHAIRS USERS IN KOREA
Bibliographic record
Abstract
More of power wheelchair users’ needs have to be met in the making of power wheelchairs. However, there has been no usability scale for the power wheelchair that is specifically focusing on the users’ needs. The purpose of this study was to test and validate a usability scale for power wheelchairs (USPW). The USPW has been developed through interviews with power wheelchair and consultations with a panel of experts. The USPW consists of four evaluation categories (safety, operation, efficiency, and satisfaction). In order to validate it, its usability and the Quebec User Evaluation of Satisfaction Assistive Technology (QUEST) were also administered to 50 power wheelchair users. The USPW was extracted 12 items from safety, 7 from operation, 5 from efficiency, and 14 from satisfaction, with a total of 38 items. The content validity met the criteria. The total internal consistency (Cronbach’s [Formula: see text]) of the USPW was 0.75. Users of power wheelchair demanded that the safety of moving on the slopes, the satisfaction of storage space for power wheelchairs, and the repair service process would be improved. The correlation between the USPW and the QUEST was significant ([Formula: see text]). The USPW may be very useful to evaluate overall usability of power wheelchair including its safety, operability, efficiency, and satisfaction. The developed usability scale (USPW) may help wheelchair makers become aware of the problems of present power wheelchairs and the users’ needs, allowing them to make better ones.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".