Adapting and validating the Satisfaction with Assistive Technology Services (SATS) scale for the Korean context
Bibliographic record
Abstract
This study aimed to culturally adapt and validate the Satisfaction with Assistive Technology Service (SATS) instrument for the South Korean context, addressing the unique needs of assistive technology users and enhancing service-satisfaction evaluation. Following “Principles of Good Practice” guidelines, the SATS was translated into Korean, involving forward and backward translation, cognitive debriefing, and pre-testing with AT users and professionals. Reliability and validity were assessed through internal-consistency measures, test–retest reliability, and concurrent-validity comparison with the Quebec User Evaluation of Satisfaction with Assistive Technology, Korean version. The SATS-K demonstrated high reliability and validity, with a Cronbach’s alpha of 0.94 (p < 0.01). The test – retest reliability, indicated by the intraclass correlation coefficient, was 0.92 (p < 0.01). Furthermore, the Pearson coefficient of the correlation between the SATS-K and the QUEST-K scores was 0.934 (p < 0.01), highlighting strong concurrent validity. The version showed notable distinctions in “instruction & training” satisfaction compared to European versions. Despite the potential statistical benefits of excluding the “waiting time” item, its retention was deemed crucial for capturing comprehensive service-satisfaction insights, particularly for highlighting the need for addressing service-delivery times. The SATS-K provides a culturally and linguistically adapted tool for evaluating AT-service satisfaction, offering significant insights for service improvement.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 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".