Perfil sócio demográfico e áreas de desempenho ocupacional afetadas em pacientes pós-ave atendidos por um serviço de terapia ocupacional
Bibliographic record
Abstract
Background: Stroke (cerebrovascular accident; CVA) resulting in difficulty orinability to perform functional tasks and daily tasks, making it difficult or impossible to return towork, participation in family and community. Occupational therapy interventions help to improveskills in activities of daily life, encouraging greater independence, autonomy, social participationand quality of life. Objective: To identify socio-demographic profile of patients after stroke treatedin an occupational therapy service and identify the filds most affected occupational performance.Methodology: Cross-sectional study with patients with stroke. We used an interview to collectsociodemographic information and the Canadian Occupational Performance Measure (COPM).Results: 19 patients, 9 (47.3%) were female and 10 (52.6%) were male, aged between 36 and 74years (20-39 = 21%, 40-60 = 57.89%, > 60 = 21%). 13 (68.4%) were married or had stable union.Most had finished elementary school. Individuals reported 40 areas of occupational performanceproblems, the most quoted food and gear (self-care), cooking (productivity) and write (leisureactivities). Conclusion: The results indicate the importance of rehabilitative approaches that focuson performance in these areas most affected by stroke, in order to enable greater independence,autonomy and social participation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".