Assistive technology for Para-badminton athletes: the application of the matching person and technology theoretical model in occupational therapy
Bibliographic record
Abstract
PURPOSE: The objective was to identify the AT demands of para-athletes in para-badminton and present the process of prescription, and follow-up of the AT devices. Also, to evaluate the expectations and the level of satisfaction with the service provided and the AT device. MATERIALS AND METHODS: Case study with 3 professional para-badminton athletes, that had as baseline the Matching Person and Technology (MPT) model, the Para-athlete Questionnaire, the AT Device Predisposition Assessment (ATD PA-Br), the Quebec User Evaluation of Satisfaction with AT (B-QUEST) and an Observation Script. The data were analyzed using absolute frequency statistics, and the qualitative data were grouped according to the categories of the MPT model. RESULTS: 4 AT demands were identified: 1 insole, 1 wheelchair footrest adaptation, 1 armband, and 1 lower-limb strap, with prescription, and AT follow-up by the occupational therapist. The quantitative analysis showed a gain in athletes' satisfaction with the AT devices, and in the expectations achieved with the use of the device. CONCLUSIONS: The role of the occupational therapist in parasports, based on a theoretical model, can contribute to the successful use of AT and therefore better performance of para-athletes. Studies with different populations are necessary to improve knowledge in the area.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".