Assessment of satisfaction among para-athletes in Benin and Burkina Faso regarding competitive assistive devices (wheelchairs)
Bibliographic record
Abstract
Most para-sports disciplines require specialized equipment that supports proper athletic performance, ensures comfort and safety, promotes autonomy, and enhances enjoyment from leisure to competition. The high entry-level cost of assistive devices tailored to specific needs often leads athletes to rely on locally made or improvised equipment. This study aimed to assess the satisfaction of para-athletes in Benin and Burkina Faso regarding their competition wheelchairs. A cross-sectional descriptive study was conducted among 64 athletes, including 47 wheelchair basketball players, 10 para-tennis players, and 7 para-badminton players from Benin and Burkina Faso. Data were collected using the Canadian version of the Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST 2.0) questionnaire. The study identified several factors affecting user satisfaction. Results showed high levels of dissatisfaction with the weight (84%), delivery services (78%), comfort (77%), adaptability (75%), and ease of use (67%) of the wheelchairs. The most important factors identified by athletes were “ease of use” (17%) and “comfort” (15%). Participants also reported issues related to conformity, such as inappropriate dimensions, excessive weight (6–9 kg above standard), and safety concerns (e.g., absence of safety straps and weak bumpers). The economic situation in these countries must be taken into account. Government subsidies and partnerships could help improve access to compliant assistive technologies. It is essential for stakeholders to collaborate in ensuring equitable access to specialized equipment and fostering a more inclusive society for people with disabilities.
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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 | 0.001 |
| 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 | 0.001 | 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".