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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".