Item Analysis of the Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it