Cross-Cultural Validation of Quebec User Satisfaction with Assistive Technology 2.0 for Spanish Population (QUEST-2.0 ES)
Bibliographic record
Abstract
BACKGROUND: Assistive technologies (ATs) are resources to promote the independence and participation of people with a disability. The use of standardized tools, based on outcome measures, is essential for guaranteeing high-quality rates. The Quebec User Evaluation of Satisfaction with AT-2.0 (QUEST) is a scale to assess the satisfaction of people using any AT. OBJECTIVES: To translate and culturally validate the QUEST-2.0 for the Spanish population (QUEST 2.0-ES). METHODS: A validation cross-design and descriptive study. The test-retest reliability, validity, and internal consistency of QUEST 2.0-ES were studied. It was divided into two phases: Sample 1 was formed by 26 persons; in sample 2, 30 persons participated. The conditions included neurological conditions, amputations, rare diseases, and deafness. RESULTS: Thirty-five men and 21 women participated in total. The majority of AT used were those for mobility. QUEST 2.0-ES analysis showed internal consistency values between the test (α = 0.87) and retest versions (α = 0.89). The internal consistency was high for AT (test, α = 0.83; retest, α = 0.87) and Service (test, α = 0.80; retest, α = 0.80). The temporal reliability (1-2 weeks) for test-retest was 0.88. CONCLUSION: QUEST 2.0-ES showed good psychometric properties in terms of validity and test-retest reliability, and it is a good tool to assess the user's satisfaction with ATs and services.
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 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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".