Perception of autonomy among people who use wheeled mobility assistive devices: Dependence on the type of wheeled assistive technology
Bibliographic record
Abstract
We evaluated perceived autonomy among users of different types of wheeled mobility assistive devices (WMADs) across five environments and identified the effect on user autonomy due to specific device characteristics. A study-specific questionnaire was used to assess satisfaction with autonomy of WMAD users in the Home, Buildings Outside of the Home, Outdoor Built, Outdoor Natural Environment, and Transportation. For each environment, 15 contextual factors were rated for their impact on participants' autonomy. Our results revealed that manual wheelchair with add-on (MWC+AO) users had higher overall satisfaction with their autonomy compared to other WMAD users. MWC+AO users reported higher satisfaction with autonomy due to their health conditions compared to other WMAD users across all environments. In Outdoor Natural Environments, MWC+AO users had the highest satisfaction with autonomy across all factors except for negotiating hills. When performing activities in Buildings, MWC users with and without add-ons reported higher satisfaction for all factors compared to power wheelchair users, except for maneuverability on different surfaces. Satisfaction with autonomy regarding contextual factors varied among WMAD users, however, MWC+AO(s) appeared to provide a more balanced sense of autonomy across most factors and environments. More in-depth investigations are required to evaluate impacts of add-on use on autonomy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".