Peer-Mediated Pivotal Response Treatment for Children With Autism Spectrum Disorder: Provider Perspectives on Acceptability, Feasibility, and Fit at School
Bibliographic record
Abstract
Few effective school-based interventions that target social-communication skills are available for students with autism spectrum disorder (ASD). The growing gap between interventions designed for use in research settings and the school environment is concerning for researchers and clinicians alike. Research methods that incorporate relevant stakeholders (e.g., educators, early intervention providers [EIPs]) throughout the process from intervention design to implementation help to bridge this gap. This study used content analysis of interview data to evaluate the acceptability and feasibility of a specific peer-mediated intervention (PMI) for school use for young children with ASD. We explored educators’ and EIPs’ perspectives on evidence-based practice (EBP), the components of the proposed intervention (using Pivotal Response Treatment, PRT), and the overall acceptability and feasibility of using the intervention at school, through interviews with 29 participants (24 elementary school educators and five EIPs serving children with ASD). Results indicated that stakeholders had some knowledge of PRT and found the PMI approach to be acceptable and feasible. Several potential challenges were identified with respect to typically developing peers as intervention agents. We discuss educators’ specific recommendations for intervention adaptation and provide a model for researchers and educators to collaborate in promoting optimal use of EBPs at school.
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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 | 0.029 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".