We Must Consider Infrastructure when Attempting to Scale up Autism EBIs: A Case Example from Early Intervention...

Area of Science:

• Developmental Psychology
• Public Health Systems
• Autism Spectrum Disorder Research

Background:

• Increasing interest in translating evidence-based interventions (EBIs) for young autistic children within public systems.
• Public Early Intervention (EI) systems, mandated under Part C of IDEA, serve children birth to three with developmental delays.
• Current research on training EI providers for autism EBIs is small-scale, lacking focus on system alignment and scalability.

Purpose of the Study:

• To review factors impacting EBI scale-up across public systems.
• To extend these factors to the implementation of autism EBIs within public EI systems.
• To identify implications for autism researchers and EI providers.

Main Methods:

• Review of factors uniformly impacting EBI scale-up in diverse public systems (citing Fagan, 2019).
• Framework extension to analyze implementation of autism EBIs in public EI systems.
• Discussion of specific impacts on scaling autism EBIs for toddlers and young autistic children.

Main Results:

• Identified key factors for EBI scale-up: developer/funder capacity, public awareness/support, leadership support, community engagement, skilled workforce, and data monitoring.
• These factors are critical for aligning autism EBIs with EI systems' infrastructure.
• Challenges exist in scaling training and sustaining public health impact.

Conclusions:

• Successful scale-up of autism EBIs in EI systems depends on addressing identified systemic factors.
• A skilled workforce and robust data monitoring are crucial for effective implementation.
• Further research is needed to optimize autism EBI delivery within public EI systems for young children.