Factors of Lower Limb Prosthesis and Orthosis User's Satisfaction in Amhara National Regional State Rehabilitation Center, Ethiopia: An Institution-Based Cross-Sectional Study
Bibliographic record
Abstract
Background: Prosthesis and orthosis are assistive devices used for different forms of cases. Lower limb prostheses and orthoses are very crucial to improve patient’s ability to walking, increased level of activity and participation to enhance their quality of life. However, there is limited knowledge of workplace variables that either satisfy or dissatisfy the prosthesis and orthosis users in the regional state, Ethiopia. Therefore, this study aimed to assess the satisfaction and associated factors among lower limb prosthesis and orthosis users in Amhara National Regional State Rehabilitation Center, Ethiopia. Methods: Institution based cross-sectional study was conducted from April 01 to May 30, 2019, at Amhara National Regional State Rehabilitation Center among lower limb prosthesis and orthosis users. Data were collected using a structured interviewer-administered questionnaire by the standardized assessment tool; Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST 2.0). The study included a total of 207 participants. Binary logistic regression analysis was performed. In the multivariable logistic regression analysis, p value < 0.05 and adjusted odds ratio (AOR) with 95% confidence interval (CI) were used to identify the associated factors. Results: The study revealed that 56.5% (95% CI: 49.9-63.3) of lower limb prosthesis and orthosis users were satisfied. Experiencing pain (AOR: 5.56, 95%CI: 2.68-11.52), living in the rural area (AOR: 3.52, 95%CI: 1.51-8.21), use of prosthesis and orthosis devices on an average less than 9 hours (AOR=0.30, 95% CI 0.11-0.80) and 9-12 hours in a day (AOR: 0.16, 95% CI: 0.07-0.38) were factors significantly associated with satisfaction. Conclusion: The magnitude of lower limb prosthesis and orthosis users’ satisfaction was found to be relatively low. Therefore, healthcare providers and managers of the rehabilitation center need to emphasize on status of the client’s device throughout patient care in the management of clients’ pain and use of devices on an average hour per day.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".