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Arely M. Hurtado 1,2 , Phillip D. Akutsu 2 , & Deanna L. Stammer 1

Predicting Juvenile Arrest Among Culturally D iverse Youth Referred for M ental Health Treatment. Arely M. Hurtado 1,2 , Phillip D. Akutsu 2 , & Deanna L. Stammer 1 Uplift Family Services 1 & California State University, Sacramento 2. INTRODUCTION.

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Arely M. Hurtado 1,2 , Phillip D. Akutsu 2 , & Deanna L. Stammer 1

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  1. Predicting Juvenile Arrest Among Culturally Diverse Youth Referred for Mental Health Treatment Arely M. Hurtado1,2, Phillip D. Akutsu2, & Deanna L. Stammer1 Uplift Family Services1& California State University, Sacramento2

  2. INTRODUCTION • Up to 70% in juvenile justice have a mental health (MH) problem • Mental health services are often inadequate or unavailable (U.S. Department of Justice, 2011) • Incarcerated youth continue to have problems throughout their life (Health Policy Institute, 2019) • Risk factors include: • Individual (e.g., gender, race/ethnicity) • Peer (e.g., delinquent or aggressive peers) • Family (e.g., parental criminality) • School (e.g., attendance) • Community (e.g., instability)

  3. Hypotheses • Positive Predictors • Demographics • Males • Ethnic Minorities • Middle Age • Older Age • Living Situation: Temporary • Psychosocial Issues • Externalizing MH Problems • Substance Abuse • History of Delinquency • Delinquency Influences • Violence History • School Non-attendance • Negative Predictors • Demographics • Younger Age • Living Situation: Permanent • Psychosocial Issues • Internalizing MH Problems • Resiliency to Violence

  4. Method: Participants • Participants (N = 1862) • First-time youth (12-19 y/o) referred to a single mental health network in California (2012-2017).

  5. meTHOD: mEASUREs • Child and Adolescent Needs and Strengths (CANS) scale • 4-point scale • Range: 0 = No Problem to 3 = Severe Problem • Community Life • Higher scores: Greater community involvement • Assessment: First month • Juvenile Arrest: Yes/No • Self-Report

  6. Results: Descriptive statistics

  7. Predicting Juvenile Arrest • Logistic regression model • Significant: χ²(11, N = 1,862) = 530.01, p < .001 • Cox & Snell pseudo-R²= .25. * p < .05, ** p < .01, *** p < .001 Note: Different asterisks denotes level of significance.

  8. DISCUSSION • Hypotheses: Partially supported • Strongest predictors • Substance Abuse • Middle age • Externalizing MH problems • History of Delinquency • Limitations • Low levels of arrest: 15% • Self-Report • Correlational, not causal relationship • Type of arrest?

  9. RECOMMENDATIONS • Age effects • Tailor treatment plans to specific age groups • Higher arrest, higher recidivism • Significance of living situation • Higher client monitoring: ↓ Juvenile arrest • Externalizing MH problems • Co-occurring externalizing MH problems and substance abuse

  10. THANK YOU!

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