Music Therapy in Enhancing Learning Attention of Children with Intellectual Disability
Bibliographic record
Abstract
Inattention is one of the significant problems that inhibit learning among children with intellectual disabilities. However, several strategies and therapies have been developed to solve the problem. This study, therefore, investigates the effectiveness of music therapy in enhancing attention among children with intellectual disability. A pretest-posttest control experimental research design was adopted. The experiment was carried out for six weeks using Music Therapy Treatment Package on 24 children with intellectual disability that were randomly selected Modupe Cole Momerial Childcare and Treatment Home/School, Akoka, Yaba, Lagos. A validated Attention Observation Rating Scale (AORS) with a reliability coefficient of 0.88 was used for this study. Three hypotheses were tested in the study, and Analysis of Covariance (ANCOVA) was used for data analysis. This study revealed that music therapy is effective in enhancing attention among children with intellectual disabilities. Sex and level of severity of the disability were also tested as moderator variables, but they have no significant main or interaction effect with music therapy in enhancing attention for children with intellectual disability. The finding is that music therapy is significantly effective in enhancing attention for children with intellectual disability regardless of their sex or level of severity. It was concluded that attention deficit could be improved for children with intellectual disability. Therefore, Music therapy was recommended for use in the school with adequate teacher training.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".