Family Quality of Life and ASD: The Role of Child Adaptive Functioning and Behavior Problems
Bibliographic record
Abstract
The family is the key support network for children with autism spectrum disorder (ASD), in many cases into adulthood. The Family Quality of Life (FQOL) construct encompasses family satisfaction with both internal and external dynamics, as well as support availability. Therefore, although these families face considerable risk in raising a child with a disability, the FQOL outcome is conceptualized as representative of a continuum of family adaptation. This study examined the role of child characteristics, including adaptive functioning and behaviour problems, in relation to FQOL. Eighty-four caregivers of children and adolescents (range = 6-18 years) with ASD participated, completing questionnaires online and by telephone. Adaptive functioning, and specifically daily living skills, emerged as a significant predictor of FQOL satisfaction, after accounting for behavioural and demographic characteristics, including child age, gender, perceived disability severity, and behavioural problems, as well as family income. Furthermore, there were significant differences across each domain of FQOL when groups were separated by daily living skill functioning level ('low,' 'moderately low,' and 'adequate'). The results suggest that intervention strategies targeting daily living skills will likely have beneficial effects for both individual and family well-being, and may reduce family support demands.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".