A systematic review of behaviour analytic interventions for young children with intellectual disabilities
Bibliographic record
Abstract
BACKGROUND: According to several comprehensive systematic and narrative reviews, interventions based on applied behaviour analysis principles, or behaviour analytic interventions, are considered evidence based for children with autism spectrum disorder (ASD). However, no comprehensive review of the literature related to behaviour analytic interventions for children with intellectual disability (ID) currently exists. METHODS: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (registration ID: CRD42018099317), the purpose of this study was to conduct a systematic review of the relevant published literature on the use of behaviour analytic interventions to develop skills in young children (0-8 years) with ID (and without ASD). A preliminary search of the literature identified 1209 potential studies published between January 2000 and April 2020. The review process resulted in 48 articles consisting of 49 studies (i.e. one paper contained two studies) that met the inclusion criteria. Most used single-case research designs. Studies were evaluated on five dimensions of methodological quality based on the Scientific Merit Rating Scale developed by the National Autism Center (NAC). The NAC definitions were also used for the quantity and quality of research evidence required for interventions to be considered established or emerging. RESULTS: There were a number of limitations to the quality of the body of research. Nevertheless, various behaviour analytic interventions met criteria for being established interventions when used for targeting communication, adaptive and pre-academic skills in young children with ID. Behaviour analytic interventions targeting academic skills met criteria for emerging interventions. CONCLUSIONS: Although the current literature is limited, results indicate that behaviour analytic interventions may be effectively used to support skill development in children with ID.
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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 | 0.031 | 0.122 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".