“If He Has it, We Know What to Do”: Parent Perspectives on Familial Risk for Autism Spectrum Disorder
Bibliographic record
Abstract
OBJECTIVE: Predictive testing for familial disorders can guide healthcare and reproductive decisions. Familial disorders with onset in childhood (e.g., autism spectrum disorder [ASD]) are promising targets for presymptomatic prediction; however, little is known about parent perceptions of risk to their children in the presymptomatic period. The current study examined risk perceptions in parents of infants at high familial risk for ASD enrolled in a longitudinal study of brain and behavior development. METHODS: Semistructured interviews were conducted with 37 parents of high-risk infants during the presymptomatic window (3-15 months) that precedes an ASD diagnosis. Infants were identified as high familial risk due to having an older sibling with ASD. Parent interview responses were coded and interpreted to distill emerging themes. RESULTS: The majority of parents were aware of the increased risk of ASD for their infants, and risk perceptions were influenced by comparisons to their older child with ASD. Parents reported a variety of negative emotions in response to perceived risk, including worry, fear, and sadness, and described impacts of perceived risk on their behavior: increased vigilance to emerging symptoms, altered reproductive and healthcare decisions, and seeking ongoing assessment through research. CONCLUSIONS: Parents of children at high familial risk for childhood-onset disorders like ASD face a period of challenging uncertainty during early development. In anticipation of a future in which presymptomatic testing for ASD is made available, it is important to understand how parents react to and cope with the elevated-but still highly uncertain-risk conveyed by family history.
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 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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".