Prostheses Use and Satisfaction among People with Lower-limb Amputation in 10 districts of Bhutan
Bibliographic record
Abstract
Introduction: The use of lower-limb prostheses restores functional mobility and improves quality of life for people with lower limb amputation. However, the use of prostheses is significantly impacted by users’ satisfaction with their prostheses and service delivery. Therefore, the excellence of prosthetic rehabilitation is not only assessed by the number of prostheses users but is also determined by the level of satisfaction with the prostheses and services received. The study was conducted to determine prostheses use and satisfaction among people with lower-limb amputation. Methods: A cross-sectional study was conducted among lower-limb prosthetic users in 10 districts of Bhutan. Data was collected by face-to-face interview using the Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST) questionnaire. Participants were recruited by purposive sampling. Results: The study found that 96.4% of persons with lower-limb amputation currently used prostheses and 79% of them have used it for more than 7 hours/day. However, 44% of prostheses needed repair. The total QUEST score of satisfaction was 4.0 (SD 0.5). Conclusion: Majority of lower-limb prostheses are in use and the users reported being quite satisfied with their prostheses and service delivery. The study recommends initiating follow-up services to improve prosthetic use and overall satisfaction scores for both prostheses and service delivery.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".