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Record W3014506030 · doi:10.1177/1362361320909178

Clinical and parental predictors of emotion regulation following cognitive behaviour therapy in children with autism

2020· article· en· W3014506030 on OpenAlexafffund
Diana Tajik‐Parvinchi, Linda Farmus, Robert A. Cribbie, Carly Albaum, Jonathan A. Weiss

Bibliographic record

VenueAutism · 2020
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsYork University
FundersCanadian Institutes of Health Research
KeywordsPsychologyAutismClinical psychologyPsychological interventionPsychopathologyDevelopmental psychologyCognitionCBCLIntervention (counseling)Child Behavior ChecklistLogistic regressionAutism spectrum disorderExpressed emotionPsychiatry

Abstract

fetched live from OpenAlex

Difficulties with emotion regulation are common in children with autism. Although interventions targeting emotion regulation show promise, children’s individual treatment responses vary, and it is important to understand the factors that contribute to treatment change. The present study aimed to identify pre-treatment child characteristics and parent psychopathology that predict treatment response in a 10-week manualized cognitive behaviour therapy intervention for children with autism, 8–12 years of age. Exploratory best-subset regression analyses were first carried out to identify the optimal set of predictors. Logistic regressions were then conducted to determine whether these variables predicted reliable improvement. Outcome variables consisted of the lability/negativity and the emotion regulation subscales of the Emotion Regulation Checklist. Predictors included pre-treatment developmental, clinical, and parent psychopathology variables. Analyses revealed that youth who started the treatment with higher verbal reasoning, higher impairment in social motivation, and more anxious parents were more likely to show reliable improvements in emotion regulation. Youth who started the treatment with higher internalizing scores had lower odds of showing reliable improvement. Implications of our findings include facilitation of active involvement, avoidance of complex language, and the provision of additional supports. Further suggestions to inform clinical practice are discussed. Lay abstract Children with autism commonly experience difficulty controlling their emotions. Although existing treatments are successful in teaching critical emotion regulation skills, not all children improve. It is important to identify the factors that influence treatment response to be able to reach more children. This study aimed to identify child and parent characteristics that predict treatment response in a 10-week cognitive behaviour therapy treatment for children with autism, 8–12 years of age, and their parents. We found that youth who started the treatment with higher verbal abilities, who were more anxious in social situations, and had parents who were more anxious, were more likely to improve in learning new emotion regulation skills. We also found that children who had more physical discomforts or complaints before starting the treatment were less likely to improve in their negative expressions of emotion. Our study suggests that it is important for clinicians to promote active involvement and learning by avoiding complex language and to use more visual materials to supplement the learning process, and make sure that sessions are sensitive to the individual needs of participants.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.038
GPT teacher head0.322
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations20
Published2020
Admission routes2
Has abstractyes

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