Journal
ADMINISTRATION AND POLICY IN MENTAL HEALTH AND MENTAL HEALTH SERVICES RESEARCH
Volume 50, Issue 4, Pages 630-643Publisher
SPRINGER
DOI: 10.1007/s10488-023-01266-x
Keywords
Asian Americans; Culture-related factors; Behavioral health needs; Strengths; Classification tree model
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This study aimed to identify unique characteristics of behavioral health needs among Asian Americans compared to White and Black Americans. Results indicated that Asian Americans struggled more with cultural-related factors regardless of age, gender, or pandemic status. The study suggests that classification tree algorithms offer a promising approach to detecting behavioral health challenges and strengths based on race, ethnicity, or other characteristics.
Given the fact that experiencing pandemic-related hardship and racial discrimination worsen Asian Americans' mental health, this study aimed to identify unique characteristics of behavioral health needs among Asian Americans (N = 544) compared to White Americans (N = 78,704) and Black Americans (N = 11,252) who received publicly funded behavioral health services in Indiana before and during the COVID-19 pandemic. We used 2019-2020 Adults Needs and Strengths Assessment (ANSA) data for adults eligible for Medicaid or funding from the state behavioral health agency. Chi-squared automatic interaction detection (CHAID) was used to detect race-specific differences among demographic variables, the pandemic status, and ANSA items. Results indicated that, regardless of age, gender, or pandemic status, Asian Americans who received behavioral health services, struggled more with cultural-related factors compared to White and Black individuals. Within this context, intersections among behavioral/emotional needs (psychosis), life functioning needs (involvement in recovery, residential stability, decision making, medical/physical health), and strengths (job history, interpersonal, and spiritual) further differentiated the mental health functioning of Asian from White and Black Americans. Classification tree algorithms offer a promising approach to detecting complex behavioral health challenges and strengths of populations based on race, ethnicity, or other characteristics.
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