Expanding Research on Contextual Factors in Autism Research: What Took Us So Long?
Bibliographic record
Abstract
Although autism is a childhood-onset neurodevelopmental disorder, its features change across the life course due to a combination of individual and contextual influences. However, the influence of contextual factors on development during childhood and beyond is less frequently studied than individual factors such as genetic variants that increase autism risk, IQ, language, and autistic features. Potentially important contexts include the family environment and socioeconomic status, social networks, school, work, services, neighborhood characteristics, environmental events, and sociocultural factors. Here, we articulate the benefit of studying contextual factors, and we offer selected examples of published longitudinal autism studies that have focused on how individuals develop within context. Expanding the autism research agenda to include the broader context in which autism emerges and changes across the life course can enhance understanding of how contexts influence the heterogeneity of autism, support strengths and resilience, or amplify disabilities. We describe challenges and opportunities for future research on contextual influences and provide a list of digital resources that can be integrated into autism data sets. It is important to conceptualize contextual influences on autism development as main exposures, not only as descriptive variables or factors needing statistical control.
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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 | 0.147 | 0.179 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.016 | 0.046 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".