SENSORY INTEGRATION VERSUS COGNITIVEBEHAVIORAL THERAPY ON BEHAVIORAL ISSUES IN LEARNING-DISABLED CHILDREN
Bibliographic record
Abstract
BACKGROUND AND AIMS Number of studies demonstrated that children with learning disabilities suffers from emotional-behavioral problems, however neurophysiologic approaches are efficient to produce better health-related outcomes thus this study aimed to investigate the effectiveness of sensory integration versus cognitive-behavioral therapy on behavioral issues of learning-disabled children. METHODOLOGY A Randomized Controlled Trial included 30 learning disabled-children, diagnosed by Psychologist on the standardized criteria, divided into Group-A (n=15) and B (n=15) where Group-A performed Sensory Integration while B performed Cognitive-Behavioral Therapy for 4 weeks. Data was collected at baseline and post the intervention on Behavioral Problem Scale and Conner’s Teacher Rating Scale respectively. RESULTS Both the groups showed significant results (p<0.05), however Group-A showed marked reduction in BPS in comparison to B while CTRS was observed with slight greater improvement in Group-B than A. CONCLUSION It was concluded that sensory integration is as effective as cognitive behavioral therapy in improving behavioral problems of learning-disabled children. KEYWORDS Learning, Behavior, Children, Cognitive-Function, Disability Evaluation, Rehabilitation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".