Applied Behavior Analysis in Children and Youth with Autism Spectrum Disorders: A Scoping Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This manuscript provides a comprehensive overview of the impact of applied behavior analysis (ABA) on children and youth with autism spectrum disorders (ASD). Seven online databases and identified systematic reviews were searched for published, peer-reviewed, English-language studies examining the impact of ABA on health outcomes. Measured outcomes were classified into eight categories: cognitive, language, social/communication, problem behavior, adaptive behavior, emotional, autism symptoms, and quality of life (QoL) outcomes. Improvements were observed across seven of the eight outcome measures. There were no included studies that measured subject QoL. Moreover, of 770 included study records, only 32 (4%) assessed ABA impact, had a comparison to a control or other intervention, and did not rely on mastery of specific skills to mark improvement. Results reinforce the need for large-scale prospective studies that compare ABA with other non-ABA interventions and include measurements of subject QoL to provide policy makers with valuable information on the impacts of ABA and other existing and emerging interventions. Supplementary Information: The online version contains supplementary material available at 10.1007/s40614-022-00338-x.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it