Disparities in Transition Planning for Youth With Autism Spectrum Disorder
Bibliographic record
Abstract
OBJECTIVE: Little is known about accessibility to health care transition (HCT) services for youth with autism spectrum disorder (ASD). This study expands our understanding by examining the receipt of HCT services in youth with ASD compared with youth with other special health care needs (OSHCN). METHODS: We used the 2005-2006 National Survey of Children with Special Health Care Needs to examine receipt of HCT services for youth (aged 12-17 years) with ASD and youth with OSHCN. Logistic regression analyses explored whether individual, family, or health system factors were associated with receipt of HCT services for youth with ASD. RESULTS: Whereas half of youth with OSHCN received HCT services, less than a quarter of youth with ASD did. Only 14% of youth with ASD had a discussion with their pediatrician about transitioning to an adult provider, less than a quarter had a discussion about health insurance retention, and just under half discussed adult health care needs or were encouraged to take on appropriate responsibility. Logistic regression analyses indicated that having a developmental disability or multiple health conditions in addition to ASD and quality of health care were strong predictors of HCT, whereas demographic and family variables accounted for little variance. CONCLUSIONS: Youth with ASD experience disparities in access to HCT services. Youth with comorbid conditions are at greatest risk for poor access to HCT services and increased quality of care has a positive effect. Research is needed to understand barriers to care and develop policy and practice guidelines tailored for youth with ASD.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".