Social, communicational, and emotional effects of social skills interventions on autistic minors
Bibliographic record
Abstract
This study examines how social skills interventions affect the quality of conversation skills and emotional competence of autistic children and adolescents. These abilities can both improve quality of life and social wellbeing in autistic individuals who often experience social difficulties, thus making this a crucial field of investigation. The studies analyzed were identified on databases such as JSTOR and the NIH; we also utilized published works from the Journal of Autism and Developmental Disorders, Journal of Communication Disorders, and the Journal of Intellectual & Developmental Disability, among others. In this systematic review, 23 studies satisfied the inclusion criteria. Studies found improvements in conversation skills such as verbalizations, initiations, responses, greetings, and usage of facial expressions/gestures. Some improvements for emotional competence consist of emotion recognition, emotional expression, and internal emotion regulation. In addition, many studies found generalization—application of learned skills in similar real-life situations—of social and emotional skills over time when following up a few months post-intervention. The majority of studies had large effect sizes (paired Cohen’s d). These findings suggest that social interventions, especially those that are group- or school-based, have positive effects on social and emotional abilities in autistic minors, as well as the generalization of these skills in real life following the programs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".