Autism Spectrum Disorders and Developmental Disabilities in Children From Immigrant Families in the United States
Bibliographic record
Abstract
OBJECTIVES: Recent census data show that nearly one-quarter of US children have at least 1 immigrant parent; moreover, there has been a dramatic increase in children diagnosed with autism spectrum disorders (ASDs) and select developmental disabilities (DDs). However, little is known about access to medical home and adequacy of insurance coverage for children with ASDs and select DDs from immigrant families. METHODS: By using the 2007 National Survey of Children's Health, we compared children with ASDs and select DDs from immigrant (n = 413, foreign born or reside with at least 1 immigrant parent) and nonimmigrant (n = 5411) families on various measures of medical home and insurance coverage. We used weighted logistic regression to examine the association between immigrant family and selected outcome measures while controlling for confounding factors. RESULTS: Compared with nonimmigrant families, children with ASD and select DD from immigrant families were more than twice as likely to lack usual source of care and report physicians not spending enough time with family. Furthermore, multivariable analyses indicate that insurance coverage is an important factor in mitigating health care barriers for immigrant families. CONCLUSIONS: The study demonstrates important areas of deficits in the health care experiences of children with ASD and select DD from immigrant households. Public policy implications include increasing access to existing insurance programs, augmenting public awareness resources for ASD and select DD, and offering assistance to immigrant families that are struggling with the medical needs of their children.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".