Maternal Emotion Socialization and Child Problem Behaviours in an Autism Spectrum Disorder Population: The Role of the Broad Autism Phenotype and Distress
Bibliographic record
Abstract
The current study examined emotion socialization (ES) processes in mothers of children with autism spectrum disorder (ASD) using a quantitative and qualitative approach. The quantitative methodology was used to explore ES practices and the outcome of child problem behaviours, while taking into account maternal characteristics of the broad autism phenotype (BAP) and distress (stress, anxiety, depression and parenting stress). For the quantitative portion of the study, participants included 57 mothers of children age 6 to 16 years diagnosed with high functioning ASD. Mothers were separated into groups: without BAP status group and with BAP status group. The results revealed that ES practices alone did not predict child problem behaviours. However, with the inclusion of distress as a moderator, the relation between ES and problem behaviours revealed differences between the BAP groups. That is, in mothers without BAP status, when predicting child problem behaviours, stress moderated emotion coaching, supportive reactions, and positive expressiveness. Anxiety and parenting stress also moderated emotion coaching. In mothers with BAP status, stress and parenting stress moderated the relation between negative expressiveness and child problem behaviours. Within the qualitative framework of the study, a thematic analysis revealed that mothers of children with ASD use a number of ES approaches when their children are experiencing negative emotions. Themes consistent with ES practices within typically developing populations emerged, as well as additional themes that have not been adequately captured in the literature (e.g., socialization that often accommodates children’s behavioural and emotional challenges) and therefore may be more unique to mothers parenting children with ASD. Clinical implications and future directions 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".