User satisfaction with mobility assistive devices: An important element in the rehabilitation process
Bibliographic record
Abstract
BACKGROUND: An assistive device often means an evident change in a person's ability, more easy to notice than the effects of most of other types of physiotherapy or occupational therapy intervention. In spite of this, there is very little evidence in this area. PURPOSE: The objective was to follow-up user satisfaction with and the use and usefulness of rollators and manual wheelchairs. The objective was also to determine any difference in satisfaction between users of the two different types of mobility assistive products. METHODS: A random sample of 262 users participated in the study, 175 rollator users and 87 wheelchair users. The Quebec User Evaluation of Satisfaction with Assistive Technology-QUEST 2.0 and an additional questionnaire were used for data collection. RESULTS: Overall satisfaction with both types of device was high and most clients reported use of their device on a daily basis. There was a difference in how the users estimated the usefulness and other characteristics as well as some service aspects related to prescription and use of the two types of device. Most users reported not having had any follow-up; however, most users had not experienced any need for one. CONCLUSIONS: A standardized follow-up will give rehabilitation professionals continuous and valuable information about the effect of and satisfaction with assistive devices.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".