Stop. Breathe. Be. A pilot study examining mindfulness training to improve the socioemotional wellbeing of youth with autism spectrum disorder
Bibliographic record
Abstract
Adolescence is challenging time for youth with autism spectrum disorder (ASD), who generally exhibit a myriad of psychosocial difficulties. While this developmental period represents an important window for intervention, few evidence-based programs exist. Recent research suggests that interventions targeting emotion regulation (ER) skill deficits in ASD may represent a promising approach to promoting more favourable outcomes for these youth (Mazefsky et al., 2014). Nurturing mindfulness has been shown to be an effective means of improving ER and wellbeing in diverse child and adult populations, although research in ASD is limited. This pilot study evaluated the impact of a 9-week mindfulness intervention on the ER and socioemotional functioning of 14 adolescents (13-17 years) with high functioning ASD using a pre-test post-test design. Parents reported statistically significant changes of small to medium effect size in adolescents’ overall problem behaviours and social skills, ER, adaptability, hyperactivity, and withdrawal behaviours. Additionally, parents reported changes of small effect size that approached significance for adolescents’ anxiety symptoms and atypicality. Adolescents reported changes of small effect size that were statistically significant for anxiety symptoms and interpersonal functioning, and non-significant for depression and social stress symptoms. Changes in many parent-reported outcome variables showed moderate to strong correlations with home practice adherence and parent-reported changes in ER. Qualitative observations of program impact and social acceptability were positive and supported the quantitative findings. The results provide promising evidence for mindfulness training with youth with ASD. Implications for assessment, intervention, and future research are discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".