Systematic reviews of psychosocial interventions for autism: an umbrella review
Bibliographic record
Abstract
AIM: A wide range of psychosocial interventions for the treatment of individuals with autism-spectrum disorders (ASDs) have been evaluated in systematic reviews. We conducted an umbrella review of systematic reviews of the effectiveness of psychosocial interventions for ASD. METHOD: Comprehensive searches were conducted in 25 bibliographic databases, relevant journals and reference lists up to May 2007. Studies included were systematic reviews on any psychosocial intervention for individuals with ASDs. Two reviewers independently assessed study relevance and quality. RESULTS: Thirty systematic reviews were included. The majority of reviews evaluated interventions based on behavioural theory (n=9) or communication-focused (n=7) therapies. Positive intervention outcomes were reported in the majority of the reviews. Methodological quality of the reviews was generally poor. INTERPRETATION: The reviews reported positive outcomes for many of the interventions, suggesting that some form of treatment is favourable over no treatment. However, there is little evidence for the relative effectiveness of these treatment options. Many of the systematic reviews had methodological weaknesses that make them vulnerable to bias. There is a need for further systematic reviews that adhere to strict scientific methods and for primary studies that make direct comparisons between different treatment options.
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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 | 0.040 | 0.139 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.009 |
| Bibliometrics | 0.029 | 0.028 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".