A systematic review of the psychometric properties of Quebec user evaluation of satisfaction with assistive technology (QUEST)
Bibliographic record
Abstract
PURPOSE: The aim of this systematic review was to evaluate the psychometric properties of the Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST). MATERIALS AND METHODS: Searches were conducted in August 2021 on four electronic databases: MEDLINE, CINAHL, Scopus, and Web of Science. Eligible papers included cross-sectional validation studies evaluating the psychometric properties of all QUEST versions. Cronbach's alpha, intraclass correlation coefficient, and comparison tools were reported. Study quality and risk of bias were assessed using the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) checklist. RESULTS: Nineteen studies were included in this systematic review. Results showed that the QUEST and QUEST 2.0 were available in 10 languages, and most validation studies analysed this tool in patients using mobility devices in various clinical conditions. One article analysed the child version (QUEST 2.1) in English. The most analysed psychometric property was Cronbach's alpha for internal consistency in 14 out of 19 studies, with values ranging between 0.74 and 0.79. Overall, 17 out of 19 studies were of adequate quality, though responsiveness was never studied. CONCLUSION: Our systematic review showed that the QUEST and its subsequent versions are reliable and valid measurement instruments to evaluate satisfaction in patients with different disabilities using various assistive technologies. This study provides useful information on the instrument's psychometric properties in different populations and cultures.
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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 | 0.019 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".