Using stakeholder perspectives to guide systematic adaptation of an autism mental health intervention for Latinx families: A qualitative study.
Colby ChlebowskiEliana Hurwich-ReissBlanche WrightLauren Brookman-FrazeePublished in: Journal of community psychology (2019)
Embedded within a Hybrid Type 1 randomized effectiveness-implementation trial in publicly funded mental health services, the current study identified stakeholder recommendations to inform cultural adaptations to An Individualized Mental Health Intervention for Autism Spectrum Disorder (AIM HI) for Latinx and Spanish-speaking families. Recommendations were collected through focus groups with therapists (n = 17) and semi-structured interviews with Latinx parents (n = 29). Relevant themes were identified through a rapid assessment analysis process and thematic coding of interviews. Adaptations were classified according to the Framework for Reporting Adaptations and Modifications-Enhanced (FRAME) to facilitate fit, acceptability, and sustained implementation of AIM HI and classify the content, nature, and goals of the adaptations. Recommended adaptations were classified through FRAME as tailoring training and intervention materials, changing packaging or materials, extending intervention pacing, and integrating supplemental training strategies. Goals for adaptations included improving fit for stakeholders, increasing parent engagement, and enhancing intervention effectiveness. The current study illustrates the process of embedding an iterative process of intervention adaptation within a hybrid effectiveness-implementation trial. The next steps in this study are to integrate findings with implementation process data from the parent trial to develop a cultural enhancement to AIM HI and test the enhancement in a Hybrid Type 3 implementation-effectiveness trial.
Keyphrases
- randomized controlled trial
- study protocol
- mental health
- phase iii
- primary care
- autism spectrum disorder
- healthcare
- phase ii
- clinical trial
- high intensity
- systematic review
- open label
- magnetic resonance imaging
- clinical practice
- heart failure
- mental illness
- big data
- left ventricular
- intellectual disability
- deep learning
- artificial intelligence
- drug induced
- image quality