Investigating the Associations Between Child Autistic Symptoms, Socioeconomic Context, and Family Life: A Pilot Study
Bibliographic record
Abstract
Objective: The day-to-day experience of families with an Autistic child may be shaped by both, child characteristics and available resources, which often are influenced by the socioeconomic context of the family. Using a socioecological approach, this study explored the quantitative associations between child autistic symptoms, family socioeconomic status, and family life. Methods: Data came from the Pediatric Autism Research Cohort—PARC Study (pilot). Parents of children with a recent diagnosis of autism completed a set of assessments, including the Autism Family Experience Questionnaire, Autism Impact Measure, and a Sociodemographic Questionnaire. A series of multiple, iterative linear regression models were constructed to ascertain quantitative associations between child autistic symptoms, socioeconomic context, and family life. Results: A total of 50 children (mean age: 76 months; SD: 9.5 months; and 84% male) with data on the variables of interest were included in the analysis. The frequency of child autistic symptoms was associated with family life outcomes (p = 0.02 and R2 = 24%). Once autistic symptom frequency, symptom impact, and sociodemographic variables were considered, parents of higher educational attainment reported worse family life outcomes compared to their lesser-educated counterparts. This cumulative regression model had considerable explanatory capability (p = 0.01, R2 = 40%). Conclusion: This study demonstrates the utility of using a socioecological approach to examine the dynamic interplay between child characteristics and family circumstances. Our findings suggest that family life for parents (of an autistic child) who have obtained higher education is reported (by the parents themselves) as less satisfactory compared to that of parents without higher education, once adjusted for the autistic symptom frequency of child, symptom impact, and income. These findings can inform the design and delivery of more family-centered care pathways during the years following a diagnosis of autism.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".