Cross-cultural adaptation and validation of the Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST 2.0): the development of the Taiwanese version
Bibliographic record
Abstract
OBJECTIVE: To develop and validate a cross-cultural version of the Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST 2.0) for users of assistive technology devices in Taiwan. DESIGN: A cross-sectional survey. PROCEDURES: The standard cultural adaptation procedure was used for questionnaire translation and cultural item design. A field test was then conducted for item selection and psychometric properties testing. SUBJECTS: One hundred and five volunteer assistive device users in community. MAIN OUTCOME MEASURES: A questionnaire comprising 12 items of the QUEST 2.0 and 16 culture-specific items. RESULTS: One culture-specific item, 'Cost', was selected based on eight criteria and added to the QUEST 2.0 (12 items) to formulate the Taiwanese version of QUEST 2.0 (T-QUEST). The T-QUEST consisted of 13 items which were classified into two domains: device (8 items) and service (5 items). The internal consistencies of the device, service and total T-QUEST scores were 0.87, 0.84 and 0.90, respectively. The device, services and total T-QUEST scores achieved good test-retest stability (intraclass correlation coefficient (ICC) 0.90, 0.97, 0.95). Exploratory factor analysis revealed that T-QUEST had a two-factor structure for device and service in the construct of user satisfaction (53.42% of the variance explained). CONCLUSIONS: Users of assistive device in different culture may have different concerns regarding satisfaction. T-QUEST is the first published version of QUEST with culture-specific items added to the original translated items of QUEST 2.0. T-QUEST was a valid and reliable tool for measuring user satisfaction among Mandarin-speaking individuals using various kinds of 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 | 0.006 | 0.011 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".