User Satisfaction with Orthotic Devices and Service in
Bibliographic record
Abstract
User satisfaction is afforded considerable importance as an outcome measurement in evidence-based healthcare and the client-centered approach. Several studies have investigated user satisfaction with orthoses. Few studies have investigated user satisfaction with orthoses in Taiwan. Therefore, the purpose of this study was to investigate the user satisfaction with orthotic devices and service using the Taiwanese version of Quebec User Evaluation of Satisfaction with Assistive Technology. We conducted a cross-sectional study of 280 subjects who had used orthoses and received services. The results showed that the mean satisfaction score was 3.74 for the devices and 3.56 for service. Concerning the participants, 69.1% and 59.6 % were quite satisfied or very satisfied with their devices and service, respectively. The satisfaction score of orthotic service was lower than that of the devices. Regarding demographic characteristics, participants living in different areas differed only in service score (p = 0.002). The participants living in eastern area and offshore islands were the least satisfied with the orthotic service. For clinical characteristics, there was a significant difference in satisfaction scores among severity of disability (all p = 0.015), types of orthoses (all p = 0.001), and duration of usage (all p = 0.001). The participants with mild disability, wearing the pressure garment and using the orthosis for less than one year, were the most satisfied with their orthotic devices and service. There is a need for improved orthotic devices and services, especially with respect to the comfort of the devices and the provision of subsidy funding.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".