Screening and treatment of trauma-related symptoms in youth with autism spectrum disorder among community providers in the United States
Bibliographic record
Abstract
Using a cross-sectional survey of 673 multidisciplinary autism spectrum disorder providers recruited from five different sites in the United States, we examined the frequency with which community-based providers inquire about, screen, and treat trauma-related symptoms in their patients/students and assessed their perceptions regarding the need for and barriers to providing these services. Univariate and bivariate frequencies of self-reported trauma service provision, training needs, and barriers were estimated. Multivariable logistic regressions identified provider and patient-related factors associated with trauma-related symptoms screening and treatment. Over 50% of providers reported some screening and treatment of trauma-related symptoms in youth with autism spectrum disorder. Over 70% informally inquired about trauma-related symptoms; only 10% universally screened. Screening and treatment varied by provider discipline, setting, amount of interaction, and years of experience with autism spectrum disorder, as well as by patient/student sex, ethnicity, and socioeconomic status. Most providers agreed that trauma screening is a needed service impeded by inadequate provider training in trauma identification and treatment. The findings indicate that community providers in the United States of varied disciplines are assessing and treating trauma-related symptoms in youth with autism spectrum disorder, and that evidence-based approaches are needed to inform and maximize these efforts.
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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 | 0.005 |
| 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".