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The Constance and Martin Silver Center on Data Science and Social Equity (C+M Silver Center) at the NYU Silver School of Social Work supports scholarship at the intersections of data science, social equity, and social work to achieve broad and transformational social impact. This symposium presents new research and methodological training initiatives in data science and social equity that are being supported by the C+M Silver Center and NYU Silver.
The first paper brings together a team of data scientists, health policy experts, and health services researchers to leverage 'big data' to identify geographical areas of mental health need and drivers of outpatient mental health service use among young adults with serious mental illness (SMI). These analyses rely on New York State Medicaid claims data. Findings emphasize the role of individual, clinical, and community-level factors as drivers of outpatient mental health service use and unmet need characterized by high prevalence of SMI, high psychiatric inpatient and emergency department use, and low outpatient visits. Leveraging 'big data' can help target and allocate limited resources to geographic areas with unmet mental health need.
The second paper presents a suicide risk forecasting tool for youth and investigates potential racial biases in algorithmic prediction. Using data from the Youth Risk Behavior Surveillance Survey, the study evaluates the main variables impacting suicide attempts across all race/ethnic groups and uses machine learning to model the predictive value of different risk factors. The study suggests that the monitoring of these risk behaviors must be taken seriously for adolescents who engage in suicide attempts, regardless of their race/ethnicity.
Finally, the third paper describes the effort to democratize data science methods by organizing the Summer Institute of Computational Social Science (SICSS)-NYU Silver with social work PhD students. This study includes a literature review and focus group discussions with participating students. This paper aims to provide practical guidance for social work educators to incorporate data science into curricula and better prepare future generations of social work researchers to address complex social issues.
The symposium seeks to advance understanding of the use of data science methods in social work research that centers social equity, as well as challenges and opportunities for the future of the field.