Effectiveness of Psycho-Correctional Methods and Technologies in Work with Children who have Autism: Systematic Review
Bibliographic record
Abstract
Background: Proper care for children with autism spectrum disorder can help reduce the difficulties faced by autistic people throughout their life. This fact causes the necessity to study the most effective therapy, teaching and development methods, social interaction skills, and emotional intelligence for children with autism. Objective: This article is aimed at identifying the most effective methods and technologies in working with children who have autism that was described in the scientific works and at revealing reasons and necessities for their more detailed study as well as possibilities to implement them. Methods: Using methods analysis, comparison and analogy, statistical method, and generalization, several publications dedicated to correction methods in work with children who have autism were selected. The most common correction methods were indicated and described according to efficacy and frequency. Results: A thorough review of research, publications, and available information on the Internet were conducted. The most effective methods and technologies in working with autistic children were identified, which included applied behavioral therapy, treatment and education of autistic and related communication-handicapped children, Floortime, parent-child interaction therapy, method Tomatis, and the program Son-rise. Recent intelligent technologies were also considered, particularly Smart Platforms for Research, intervention, and Neurodevelopmental growth, the use of virtual reality, and the program Empowered Brain technology. Conclusions: The authors emphasize the necessity of thorough psychodiagnostics and the formation of psycho-correctional tasks based on exclusively personal needs, symptoms of the disorder, and disease etiology. The analysis of modern psycho-correctional technologies leads to the conclusion that their effectiveness depends, first of all, on the correct expedient application in each particular case.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".