Cognitive Behavioural Therapy Techniques for Treating Verbal Impairments in Children Diagnosed with Autism Spectrum Disorder - A Review
Bibliographic record
Abstract
This paper aims to provide an overview of current research in cognitive behavioural interventions which address verbal impairment in children with autism. The studies are evaluated based on methodological quality and the validity of the data collected. Studies examining behavioural interventions for children with autism were selected from a number of databases, namely Ovid, PsychINFO, and Embase. Multiple filtration rounds were conducted to ensure that papers met the inclusion criteria, followed the DSM IV autism definition, and met the methodological quality standards. A CONSORT style observational longitudinal checklist was used to evaluate the methodological quality of the studies. Criteria pertaining to study designs were more commonly addressed than those focusing on internal validity. Analysis of literature subsequent to the year 2000 demonstrated an emergence of behavioural therapies focused on remediating verbal impairment in children diagnosed with autism. Common limitations amongst all reviewed papers were discussed in terms of impact on validity and reliability. Finally, the discussion consolidated the future directives noted in all papers to discuss trajectories for further research. The chosen literature often neglected to include essential quantitative information that affected their validity. Inclusion of control groups and appropriate sample sizes should be investigated, and future directions of this research should include the use of a diverse sample that is representative of different ethnic backgrounds and socioeconomic statuses.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".