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.IMPLICATION FOR REHABILITATIONThis systematic review verify the appropriateness of the “Quebec User Evaluation of Satisfaction with Assistive Technology” (QUEST), as a measure of satisfaction;This systematic review allow clinicians to keep up to date with new versions of the tool, new countries of validation and population in which it can be used.This study supports clinicians in making informed decisions when choosing assessment tools.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.000 | 0.008 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".