Validation of the Greek version of the device subscale of the Quebec User Evaluation of Satisfaction with Assistive Technology 2.0 (QUEST 2.0)
Bibliographic record
Abstract
The purpose of the study was to evaluate the device subscale of the QUEST 2.0 instrument and provide evidence for the validity and reliability of the Greek version. To this end, a cross-cultural adaptation was performed. Field test studies were conducted to validate the appropriateness of the final outcome. Data were drawn from a study of 115 subjects who had been administered the GR-QUEST questionnaire. Ratings related to the different items were statistically analyzed. The exploratory factor analysis with varimax rotation conducted revealed a three factors structure of the device subscale in contrast with previous studies. Our "Safe Use" subscale contains the items adjustments, safety and effectiveness of the original instrument, the "Fit to Use" subscale contains the dimensions, weight and ease of use items, and the "Endurance" subscale contains the items durability and comfort of the original questionnaire. Reliability measures (ICC=0.949, Pearson´s correlation=0.903, Cronbach´s α=0.754) yielded high values. Test-retest outcome showed great stability. Based on the results, the GR-QUEST can be considered as a valid and reliable instrument and thus it can be used to measure the satisfaction of patients with assistive devices, while it is applicable to the Greek population. Further assessment of the services subscale is needed.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".