Mobility and satisfaction with lower-limb prostheses and orthoses among users in Sierra Leone: A cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: To investigate patients' mobility and satisfaction with their lower-limb prosthetic or orthotic device and related service delivery in Sierra Leone; to compare groups of patients regarding type and level of assistive device, gender, area of residence, income; and to identify factors associated with satisfaction with the assistive device and service. METHODS: A total of 139 patients answered questionnaires, including the Quebec User Evaluation of Satisfaction with Assistive Technology questionnaire (QUEST 2.0). RESULTS: Eighty-six percent of assistive devices were in use, but half needed repair. Thirty-three percent of patients reported pain when using their assistive device. Patients had difficulties or could not walk at all on: uneven ground (65%); hills (75%); and stairs (66%). Patients were quite satisfied with their assistive device and the service (mean 3.7 out of 5 in QUEST), but reported 886 problems. Approximately half of the patients could not access services. In relation to mobility and service delivery, women, orthotic patients and patients using above-knee assistive devices had the poorest results. The general condition of the assistive device and patients' ability to walk on uneven ground were associated with satisfaction with the assistive devices and service. CONCLUSION: Patients reported high levels of mobility while using their device although they experienced pain and difficulties walking on challenging surfaces. Limitations in the effectiveness of assistive devices and limited access to follow-up services and repairs were issues desired to be addressed.
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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 | 0.000 | 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) | 0.001 | 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".