Satisfaction with informal supports predicts resilience in families of children with autism spectrum disorder
Bibliographic record
Abstract
The current study examined the role of informal supports in predicting resilience among families of children with autism spectrum disorder. Participants included 153 caregivers of children with autism spectrum disorder, who were between the ages of 2 and 18 years ( M = 10.45; SD = 4.32). Caregivers completed a measure of satisfaction with informal supports as well as the Family Resilience Assessment Scale-autism spectrum disorder. After controlling for demographic factors and child behavioural problems, regression analyses revealed that satisfaction with informal supports significantly predicted family resilience. The findings highlight the importance of specifically strengthening informal support networks when providing services and interventions to families of children with autism spectrum disorder to foster the family’s resilience. Lay abstract The study of resilience among individuals with disabilities and their families represents a paradigmatic shift from a deficits orientation towards a more holistic and contextualized approach focused on strength and adaptation. The current study investigated whether informal supports could help improve families’ capacity for resilience. We recruited 153 caregivers of children aged between 2 and 18 years who all had a diagnosis of autism. Participants were asked to complete surveys assessing resilience in their families as well as their satisfaction with informal supports (e.g. friends and family). Families more likely to report higher satisfaction with their informal support networks demonstrate greater resilience. The results suggest that informal social supports are a valuable resource for families in strengthening their capacity for resilience. The findings may help inform the development of interventions and services that work collaboratively and innovatively with families and their social networks to provide assistance and support in meaningful and effective ways.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".