A Social Work and Data-Driven Framework for Enhancing Autism Care in Marginalized Communities

This paper proposes a novel framework integrating social work and data-driven methodologies to improve autism care within marginalized communities. Recognizing the unique challenges faced by these communities, the framework leverages advanced data analytics, including machine learning and predictive models, alongside social work principles to enhance access to care, streamline support systems, and improve outcomes for individuals with autism. The approach is designed to address disparities in healthcare delivery, social stigmas, and lack of resources, which disproportionately affect underrepresented groups. By combining the strengths of both social work practices, such as community engagement and individualized support, with data-driven insights, the framework aims to create a holistic, scalable, and effective model for autism care. This research highlights the potential of data-driven decision-making to inform interventions, while emphasizing the importance of cultural competence, empathy, and community-based solutions in social work practice. The study calls for an interdisciplinary approach to autism care that bridges technological innovation with a deep understanding of the socio-cultural dynamics within marginalized communities.