Understanding the Association Between Neighborhood Resources and Trauma‐Informed Care Among Providers Who Serve Autistic Youth
Bibliographic record
Abstract
A growing body of literature suggests that youth with autism spectrum disorders (ASD), herein, autistic youth, face an increased risk of being exposed to adverse childhood experiences (ACEs). However, trauma-informed approaches to care among autistic youth remain limited. In a large cross-sectional survey of ASD providers (N = 670) recruited from five U.S. locations, we examined the association between neighborhood resources using the Child Opportunity Index (i.e., educational, health/environmental, and social/economic opportunities) and the frequency at which providers engaged in trauma-informed care (i.e., inquire about, screen for, treat, and provide referrals for trauma diagnosis and treatment) and the types of adverse childhood experiences (ACEs) they screen for (i.e., maltreatment/neglect and household dysfunction). The latent model revealed that providers in neighborhoods with fewer resources engaged in more trauma-informed care and were more likely to screen for ACEs related to household dysfunction. Follow-up exploratory analyses indicated that providers in the lowest 20% of opportunity neighborhoods made the greatest efforts in trauma screening for maltreatment and household dysfunction, followed closely by those in the lowest 40%, compared to higher-opportunity areas. Sensitivity analyses, controlling for potential nesting effects, confirmed similar results. These findings may suggest a concerted effort to ensure that autistic youth in highly disadvantaged areas receive adequate trauma screening. However, lower screening rates in higher-resourced neighborhoods may mean trauma-exposed autistic youth in these areas are overlooked. Expanding provider training to emphasize trauma inquiry across all neighborhoods could help address this gap. Limitations, implications for policy and practice, and future directions are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".