Coping, Context And Family Mental Health Within CBT For Autistic Children
Bibliographic record
Abstract
Autistic children experience elevated emotional-behavioural difficulties; links have been identified between these struggles and parent distress. Although parent-involved cognitive behavioural therapy (CBT) helps many autistic children improve overall emotional distress, there is a critical lack of understanding as to the role of children’s coping ability within this process, and the systemic factors that may impact change. To address this gap, this dissertation investigated the impact of family and child moderators of coping skill change within CBT for autistic, school-age children. Data was pooled from three related CBT interventions administered to 186 autistic children ages 8 – 13 years and their families, from 2013 – 2021. In Study 1, Exploratory Item Factor Analysis was conducted with pre-treatment data to investigate the dimensionality of the ERSSQ-P. A multidimensional structure with three factors was identified as the most appropriate fit, comprised of a 10-item subscale within the emotion regulation domain (Coping: a child’s ability to modulate distressing feelings in stressful contexts), and two subscales within the social communication domain (Initiating and Interacting). Validity for the subscales was confirmed based on associations with measures of emotion regulation, depression and social communication. Study 2 used the Coping subscale and multilevel modeling to investigate how pre-treatment child and family factors impact coping skill change for autistic children across three timepoints, and whether these relationships varied according to treatment type (individual vs. small group). Results indicated that on average, all children experienced the same, small level of improvement in coping ability, regardless of treatment type. However, pre-treatment systemic factors (child depression, restricted interests and repetitive behaviours, parent distress, family stress related to child behaviour) were related to lower initial coping ability, suggesting that children experiencing higher levels of difficulties at the beginning of treatment were also likely to finish the program at a relatively lower skill level. Although these relationships do not appear to be a barrier to statistical improvement, meaningful change may require longer than 10 weeks. Greater support for parents may also be warranted. Overall, findings present preliminary, yet practical considerations for clinicians and community agencies that can assist with targeted, strength-based treatment planning.
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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 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".