Session: WITHDRAWN: Dispelling Racial/Ethnicity Monoliths within Social Work Research, Practice, and Policy: A Case for Disaggregating Race/Ethnicity Health and Mental Health Data (Society for Social Work and Research 26th Annual Conference - Social Work Science for Racial, Social, and Political Justice)

71 WITHDRAWN: Dispelling Racial/Ethnicity Monoliths within Social Work Research, Practice, and Policy: A Case for Disaggregating Race/Ethnicity Health and Mental Health Data

Friday, January 14, 2022: 8:00 AM-9:30 AM
Independence BR H, ML 4 (Marriott Marquis Washington, DC)
Cluster: Race and Ethnicity
Melanie Morris, MSSW, Boston University, Rachel John, MSW, MPH, Boston University, Mihoko Maru, PhD, LCSW, Boston University and Gabriel Carrillo, DSW, CDFT, Saint Louis University
The social work profession has long been invested in serving communities who have been historically and systemically oppressed due to their race and ethnicity. Race and ethnicity impact the political, economic, social, and cultural life of people and are key social markers of inequity for Black, Indigenous, People of Color (BIPOC) communities. The current aggregation of race and ethnicity data prevents true understanding of inequities in health and mental health among these communities. While social workers have been called in their code of ethics to "promote sensitivity to and knowledge about oppression and cultural and ethnic diversity," past research has suggested that social work scholars have inadequately addressed race and ethnicity in research and have failed to study the diverse experiences of the BIPOC community.

Our nation's complex and oppressive history with white supremacy impacts how we collect and interpret data on race and ethnicity. BIPOC communities have historically been treated as monolithic groups, especially in national and state surveillance health surveys. For example, among Asian Americans, there is a wide spectrum of education, household income, and English language proficiency, with Asian Indians in the higher range and Bhutanese and Burmese in the lower range of these socioeconomic indicators. By aggregating these socioeconomic indicators among all Asian Americans, the specific health and mental health needs of each sub-group remain underexplored. Similarly, studying the aggregate of Latin* ethnicity masks the diversity seen in immigration history, legacies of colonialism in their countries of origin, and other structural barriers that impact health and mental health outcomes. Neglecting to examine the diversity of BIPOC communities perpetuates and upholds stereotypes and ignores the sub-group specific disparities and inequities experienced.

Through this roundtable we aim to underscore the implications of such research practice and have a salient discussion about action-oriented solutions to the disparities resulted by the social construction of race. The panel will consist of doctoral students, post-doctoral fellows, social work faculty and practitioners from the field. The panelists will discuss their personal and professional experiences navigating challenges with current aggregated race and ethnicity data.

Key questions for discussion: 1. How does our nation's complex and oppressive history with white supremacy impact how we collect data on race and ethnicity? 2. What are the current limitations of aggregated data for research, practice, and policy in the field of social work? 3. Why should social workers push for the collection and use of disaggregated data in our research, practice, and policy? 4. What are recommendations for social work to move towards promoting and collecting disaggregated race and ethnicity data?

Social work researchers need to be at the forefront of demanding disaggregation of data and finding innovative ways to collect this information. Data disaggregation allows for more accurate and targeted research that centers the lived experiences of diverse groups, while capturing and addressing the nuanced needs and disparities among these groups. As the social work field advances in social-justice oriented research, practice, and policy, data disaggregation should be adopted as a standard practice for health equity.

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