An intervention of occupational therapy in parasports using the matching person and technology model: A case study
Bibliographic record
Abstract
BACKGROUND: The practice of occupational therapy in parasports aims to improve participation in sports as an occupation, reducing barriers stemming from the environmental factors. OBJECTIVE: To analyze the process of choice, prescription and follow-up of assistive technology (AT) in competitive adolescent parasports using the theoretical model Matching Person and Technology. METHODS: Case study with 3 adolescents from 12 to 18 years old in Bocce and Para-badminton modalities was performed. Characterization Questionnaire; Quebec User Evaluation of Satisfaction with assistive Technology (B-Quest); Assistive Technology Device - Predisposition Assessment (ATD PA-Br); Brazil Criteria and Intervention Report were used. RESULTS: Level of income was intermediate and low. The AT used were handcrafted by family and coaches. The Psychosocial factors detected were low privacy, autonomy, discomfort and device appearance. Pre-intervention there was dissatisfaction with AT related to the device and the context. Post-intervention, satisfaction scores increased. CONCLUSIONS: The model was a positive guide regarding the intervention of technology in the parasport, directing the participation of the specialist with the parathletes, their family and coaches, in the continuous monitoring of its use. This was key for the satisfaction in using the AT in sports, besides contributing to their occupational performance and maintaining people with disability in parasports, increasing the possibility of adolescents becoming professional para-athletes. Further studies in this area are suggested.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".