Satisfaction with wheelchairs and associated factors among older adults with disabilities in Sichuan, China: a cross-sectional study
Bibliographic record
Abstract
There is a lack of literature examining the level of satisfaction with assistive devices among older adults with disabilities and the related factors that affect their satisfaction in China. This study aimed to assess satisfaction with wheelchairs and identify associated factors among older adults with disabilities. A cross-sectional survey was conducted in Sichuan Province, China, utilising a facility-based approach to recruit participants from rehabilitation centres and community care facilities. A total of 710 older adults with disabilities who use wheelchairs were surveyed using a baseline data questionnaire designed by the authors. Convenience sampling was employed to recruit participants. The satisfaction level was assessed using the Quebec User Evaluation of Satisfaction with Assistive Technology. K-means clustering categorised satisfaction profiles, and binary logistic regression analysed predictors. Two distinct clusters emerged: a High-Rating group (62.3%, n = 442) and a Low-Rating group (37.7%, n = 268). Formal assistive device education was the strongest predictor of high satisfaction (aOR = 2.44, 95% CI:1.56–3.84, p < 0.001), followed by self-perceived education need (aOR = 1.52, 95% CI:1.02–2.27, p = 0.041). The largest satisfaction gaps were in professional services (Δ = 1.46, Cohen’s d = 2.35), follow-up services (Δ = 1.42, d = 2.29), and ease of use (Δ = 1.45, d = 2.39). Cost dissatisfaction was pervasive across groups. Device education and service quality—particularly post-purchase support—are critical drivers of satisfaction. Policy priorities include: 1) mandatory training at device provision points, 2) standardised after-sales service systems, and 3) cost-reduction initiatives.
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How this classification was reachedexpand
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".