Reliability and validity of the Dutch version of QUEST 2.0 with users of various types of assistive devices
Bibliographic record
Abstract
PURPOSE: In this paper, the Dutch version of the Quebec User Evaluation of Satisfaction with assistive Technology (D-QUEST) is validated in users of a large variety of assistive devices (n=2002). METHOD: D-QUEST consists of a written questionnaire. The respondent rates his or her satisfaction with respect to 12 aspects on a five-point scale. Users of 10 different types of assistive devices participated. Analyses were performed for each type of assistive device. Reliability is tested by analysing internal consistency. Content validity is tested by analysing applicability of the 12 aspects. The non-applicability option for answering questions is studied. Construct validity is tested by analysing correlations with problem solving and with general satisfaction. RESULTS: Reliability proves to be good for all types of assistive devices. Including the non-applicability option improves the feasibility of the instrument without affecting content validity. Correlations between D-QUEST scores on the one hand and problem solving and general satisfaction questions on the other are as expected, supporting validity. CONCLUSIONS: D-QUEST (and therefore also QUEST) proves itself to be a highly applicable, reliable and valid instrument to assess user-satisfaction of users of all kinds of assistive device provisions.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".