O RACIOCÍNIO DA TERAPIA OCUPACIONAL COMO BASE PARA A OFERTA DAS ADAPTAÇÕES RAZOÁVEIS: ARTICULAÇÃO ENTRE LEI BRASILEIRA DE INCLUSÃO, NEUROCIÊNCIAS E OCUPAÇÕES ESCOLARES
Bibliographic record
Abstract
In Brazil, Inclusive Education has been consolidated through the Brazilian Law for the Inclusion of Persons with Disabilities (Lei Brasileira de Inclusão - LBI). In parallel, advances in Neurosciences have brought new perspectives for understanding learning. Occupational Therapy is situated within this context, using an occupation-centered approach to analyze barriers and propose interventions. This study aims to describe the clinical reasoning of Occupational Therapy practice in the school context, articulating its principles with inclusion legislation and the foundations of Neurosciences. To this end, a qualitative and descriptive experience report was conducted in a private school with a 7th-grade middle school student diagnosed with Autism Spectrum Disorder (ASD). The Collaborative Consultation model was employed, involving the occupational therapist and the school team, along with the Canadian Occupational Performance Measure (COPM) to identify impaired school occupations, and the SMART model for goal setting. The reasoning process integrated occupational analysis and neuroscientific interpretation of performance to support reasonable accommodations. Thus, the results demonstrate the effectiveness of articulating Occupational Therapy reasoning, inclusion legislation, and Neuroscience knowledge in the development of Reasonable Accommodations. It can be concluded that an interdisciplinary approach was essential to promote the student’s school participation by transforming the environment and activities to meet specific functional needs.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".