The TEACCH program for children and adults with autism: A meta-analysis of intervention studies
Bibliographic record
Abstract
The intervention program for autism known as Treatment and Education of Autistic and Related Communication Handicapped Children (TEACCH) is considered an emerging practice for autism. In the present study we used state-of-the-art meta-analytical procedures to examine the pooled clinical effects of TEACCH in a variety of outcomes. A total of 13 studies were selected for meta-analysis totaling 172 individuals with autism exposed to TEACCH. Standardized measures of perceptual, motor, adaptive, verbal and cognitive skills were identified as treatment outcomes. We used inverse-variance weighted random effects meta-analysis supplemented with quality assessment, sensitivity analysis, meta-regression, and heterogeneity and publication bias tests. The results suggested that TEACCH effects on perceptual, motor, verbal and cognitive skills were of small magnitude in the meta-analyzed studies. Effects over adaptive behavioral repertoires including communication, activities of daily living, and motor functioning were within the negligible to small range. There were moderate to large gains in social behavior and maladaptive behavior. The effects of the TEACCH program were not moderated by aspects of the intervention such as duration (total weeks), intensity (hours per week), and setting (home-based vs. center-based). While the present meta-analysis provided limited support for the TEACCH program as a comprehensive intervention, our results should be considered exploratory owing to the limited pool of studies available.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.022 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".