Translation into Arabic of the Quebec User Evaluation of Satisfaction with Assistive Technology 2.0 and validation in orthosis users
Bibliographic record
Abstract
The assessment of patient satisfaction with the orthosis is a key point for clinical practice and research, requiring the availability of questionnaires with robust psychometric properties. The aim of this study was the translation into Arabic and Rasch validation of the Quebec User Evaluation of Satisfaction with assistive Technology (A-QUEST 2.0), one of the few standardized instruments appropriate for assessment of patient satisfaction with the orthosis. The translation was carried out in accordance with guideline recommendations. The translated version was administered to a convenience sample of 100 individuals with various health conditions using orthosis (59% men, mean age 36 years). Data were analyzed using confirmatory factor analysis, followed by Rasch analysis for each of the two subscales, that is satisfaction with the Device (eight items) and with Services (four items). The results of the confirmatory factor analysis verified the bidimensionality of A-QUEST 2.0. Rasch criteria for the functioning of rating scale categories were fulfilled for both subscales. All items except one showed an adequate fit to the Rasch model. The person separation reliability for A-QUEST 2.0_Device was 2.19 and Cronbach's α 0.83; A-QUEST 2.0_Services separation reliability was 2.79 and Cronbach's α was 0.89. Thus, the two subscales could define a hierarchy of persons along each measured construct with at least three different levels of satisfaction. This Rasch validation of A-QUEST 2.0, in patients with various types of orthoses, provides additional evidence of the psychometric properties (and particularly the internal construct validity) of the questionnaire, and provides insights for further improving its metric quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".