Item Analysis of the Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST)
Bibliographic record
Abstract
The Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST) is an outcomes assessment tool designed to measure satisfaction with assistive technology in a structured and standardized way. The purpose of this article is to present the results of an analysis of the 24 items comprising QUEST and to explain how a subset of items demonstrating optimal measurement performance was selected. The criteria against which the items were measured were general acceptability, content validity, contribution to internal consistency, test-retest stability, and sensitivity. The items that ranked best in terms of these measurement properties were submitted to factorial analysis in order to complete the item selection. The first series of analyses reduced the item pool approximately by half, and the second series of analyses led to the final selection of 12 items. Factor analysis results suggested a bidimensional structure of satisfaction with assistive technology related to the assistive technology device (eight items) and services (four items). The 12-item revised version that will result from this study should prove to be a reliable and valid instrument for measuring outcomes in the field of assistive technology.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.005 | 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".