Community-based care for autistic youth: community providers’ reported use of treatment practices in the United States
Bibliographic record
Abstract
Introduction: To illustrate the landscape of community-based care for autistic youth in the United States, we identified transdisciplinary psychosocial intervention practice sets that community providers report utilizing to care for this population, and examined characteristics associated with provider-reported utilization. Methods: = 701) from allied health, behavioral, education, medical, mental health and other disciplines who treat or work with autistic youth (7-22 years) participated. Results: Exploratory factor analysis yielded four factors: Consequence-Based Strategies (CBS), Cognitive-Behavioral and Therapy Strategies (CBTS), Antecedent-Based Strategies (ABS), and Teaching Strategies (TS). Providers across disciplines reported utilizing ABS more often than other sets. Providers from behavioral disciplines, with less than 4-year or Master degrees, or with more experience reported the most use of ABS, CBS and CBTS. Medical and behavioral providers reported the most use of TS. Setting and child characteristics were associated with practice set use, indicating variability by disability and client socioeconomic status. Discussion: Findings reflect the complexity and inconsistency of the service landscape for autistic youth across the U.S. Only by understanding the service landscape and predictors of practice utilization, can researchers, policymakers, provider groups, and the autistic community facilitate effective implementation strategy development and use to ultimately improve community-based care.
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 | 0.004 |
| 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) | 0.001 | 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".