MPT Use in Paralympic Sports in Brazil
Bibliographic record
Abstract
Assistive technology (AT) devices have been shown to be indispensable in paralympic sports modalities. This chapter presents the use of the Matching Person and Technology (MPT) conceptual model in the process of prescription and implementation of AT. A cross-sectional descriptive study was used to conduct a survey of AT devices already in use or prescribed by the occupational therapist in Wheelchair Rugby, Archery, 7-a-side Soccer, and Parabadminton modalities between July 2018 and May 2019. The sample was composed of 29 parathletes who answered the Assistive Technology Device Predisposition Assessment—Brazilian Version and the Assessment of User Satisfaction with Quebec Assistive Technology (QUEST 2.0). The data were described in tables according to the modalities, followed by a frequency analysis. A variety of devices used in each modality were identified, resulting from individual (personalized) demands, and 97% of the parathletes benefited from the AT. The MPT was a guiding theoretical model and was indispensable in understanding of the context and the demands of each parathlete, which in turn facilitates the process of prescription, follow-up, use, and, above all, satisfaction with the AT device.
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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 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".