Culturally responsive interventions for autistic children and their families have been developed and implemented to address issues related to limited representation, inequities, and disparities in access to care of minoritized families in research. Currently available reviews are relatively limited in scope or do not synthesize interventions specifically. Therefore, we conducted a meta-analysis to synthesize autism intervention literature that specifically targeted autistic individuals and their family members from minoritized backgrounds, such as immigrant families. We used four databases to identify studies that used culturally responsive interventions with minoritized autistic children and their families. An article was included if it included empirical intervention data using an experimental design. A total of 354 studies were initially screened, and 24 studies were included. Effect sizes of these studies were extracted across two levels (i.e., child and family levels). Data from group design studies were extracted manually, and data from single-case design studies were extracted using a web-based tool. We used design-comparable standardized effect sizes to compare across both designs. The analysis revealed a large, positive, and significant overall effect size across culturally responsive interventions. Specifically, social-communication and mental health outcomes yielded significant effects at the child level. Additionally, parents' mental health and fidelity of strategy implementation also yielded significant results. Our results suggest that culturally responsive interventions yield comparable outcomes to unadapted, original interventions. Future research should examine the distinction between the effect of cultural adaptation and the efficacy of the intervention itself.
Keyphrases
- mental health
- physical activity
- cancer therapy
- case control
- randomized controlled trial
- healthcare
- young adults
- electronic health record
- systematic review
- big data
- quality improvement
- primary care
- type diabetes
- palliative care
- autism spectrum disorder
- artificial intelligence
- drug delivery
- single cell
- machine learning
- chronic pain
- weight loss
- deep learning
- health insurance