Satisfaction of Individuals with Physical Disabilities Regarding the Use of Assistive Technologies
Bibliographic record
Abstract
Background: The individual's personality and social factors influence a person's desire or ability to use assistive technology devices. Therefore, these components contribute to a positive or negative influence on the use of assistive technology and on the degree of satisfaction with the use.Objective: The aim of the study was to evaluate levels of satisfaction of individuals with physical disabilities with their prescribed piece of assistive technology. Methods: A quantitative questionnaire survey using The Quebec User Evaluation of Satisfaction with Assistive Technology 2.0. Data collect took place from August 2018 to April 2019, in the Physiotherapy Clinic of a Community University, in the Specialized Center for Physical and Intellectual Rehabilitation II and in the support networks for individuals with disabilities, such as: Roda Solta, Association for Supporting Families of Impaired People, Association of Disabled People and athletes of the women's Paralympic Handball Championship. Results: Fifty-six individuals with physical disabilities between 20 and 80 years old participated in the research, 27 women (48.2%) and 29 men (51.8%).The results suggested that individuals are quite satisfied with their piece of assistive technology M=3.56(SD=1.09) and more or less satisfied with the professional services provided M=3.28 (SD=1.34). Among the 12 items considered most important by the participants, durability of the resource (54%) prevailed, followed by comfort (28%) and safety (24%).Conclusions: The dissatisfaction of individuals with physical disabilities was evident both in issues related to the resource and in the area related to the professional services, in relation to the resources. It needs to be adapted to the user’s needs, always aiming to improve health condition and quality of life.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".