Effectiveness of Art-Therapy-Based Intervention Programmes for Improving Social Communication in Children with Rett Syndrome
Bibliographic record
Abstract
The research into effective art-therapy-based interventions for improving the social communication skills of children with Rett syndrome is important for the adaptation of this group of children. This study aims to evaluate the effect of a 6-month art-therapy-based intervention program based on art therapy on improving social communication in children with Rett syndrome. The research employed a quasi-experimental method, direct (unstructured) observation, a standardized Social Responsiveness Scale, and mathematical and statistical data processing methods (Levene test, paired sample t-test). The results showed a significant improvement in social communication in the experimental group (EG) after the intervention, as evidenced by paired and independent sample t-tests. This indicates statistically significant differences between pre-and post-test scores in the EG (mean difference 14.525 with a standard deviation of 22.592). The standard error for this group was 3.572, and the 95% confidence interval for the mean difference ranged from 7.300 to 21.750. The Student's t-test reached 4.066 with 39 degrees of freedom, resulting in a two-tailed p-value of less than 0.001. It has been found that art therapy can significantly improve social communication and emotional regulation subscales in children with Rett syndrome. The obtained data indicate the need to include therapeutic strategies based on art therapy in intervention programs for children with Rett syndrome. Prospects for further research are based on studying the impact of art therapy and other interventions not only on social communication but also on the cognitive development of children with Rett syndrome.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".