Asian American (AA) students in academically high-performing schools are thought to represent a high-risk but underidentified group for mental health need and potential suicide risk. Previous data indicate that internalizing mental health needs among AA students are more likely to go unmet compared with other racial/ethnic groups. This is the first study to examine disparities in rates of follow-up mental health services (MHS) for AA students assessed for suicide risk in schools. We examined rates of parental consent for MHS and ultimate linkage to care following risk assessments for students in an ethnically diverse, high-performing school district. Findings indicated that AA students were underrepresented among suicide risk assessments compared with their district enrollment. Although female students were more often referred for suicide risk assessments, AA boys appeared at heightened risk compared with boys from other racial/ethnic groups at the elementary and middle school levels. In terms of rates of MHS receipt, 43.4% of AA students and 57.1% of Latino students received new or ongoing MHS following risk assessment. Multinomial logistic regressions revealed racial/ethnic disparities in parental consent and linkage to care, such that AA students were at significantly higher risk of having parents decline MHS (relative risk ratio = .26, p < .001) and having no initiation of MHS following risk assessment (relative risk ratio = .55, p < .01) compared with Latino students. Potential barriers to parental consent and aftercare for AA families are discussed, highlighting the need for implementation strategies to reduce racial/ethnic disparities for youth at risk of suicide.
Advances in computer science and data-analytic methods are driving a new era in mental health research and application. Artificial intelligence (AI) technologies hold the potential to enhance the assessment, diagnosis, and treatment of people experiencing mental health problems and to increase the reach and impact of mental health care. However, AI applications will not mitigate mental health disparities if they are built from historical data that reflect underlying social biases and inequities. AI models biased against sensitive classes could reinforce and even perpetuate existing inequities if these models create legacies that differentially impact who is diagnosed and treated, and how effectively. The current article reviews the health-equity implications of applying AI to mental health problems, outlines state-of-the-art methods for assessing and mitigating algorithmic bias, and presents a call to action to guide the development of fair-aware AI in psychological science.
Highlights
Early life stress is associated with aberrant neural function.
Altered neural function is apparent as young as early school age.
Reward- and emotion-related regions show exaggerated activation/connectivity.
Findings remain when controlling for concurrent stressful life events.
Parents who were abused as children are at increased risk for perpetuating maladaptive parenting practices, yet the mechanisms underlying this relationship remain unclear. This study prospectively examined maternal distress (a latent variable consisting of depressive symptoms and daily stress) and family violence as potential mediators in the intergenerational transmission of abusive (i.e., psychologically aggressive and physically assaultive) parenting. Participants included ( N = 768) mother-child dyads identified as being at-risk for family violence and maltreatment prior to children’s age four. More maternal childhood abuse was associated with more distress and increased risk for family violence exposure in adulthood. However, only maternal distress mediated the association between mothers’ history of abuse and their use of abusive parenting strategies. This study provides critical information about ecological mechanisms underlying the intergenerational transmission of abusive parenting and suggests the importance of targeting depression and stress management among mothers with abuse histories to curtail the cycle of violence.
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