Risk, Need, and Racial Inequality - A Machine Learning Analysis of Rearrest in Juvenile Drug Treatment Courts and Traditional Juvenile Courts

Aug 5, 2026

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The juvenile justice system shapes youths’ trajectories, and punitive interventions can undermine rehabilitative outcomes. The study examines whether Juvenile Drug Treatment Courts (JDTCs) deliver more risk-need tailored programming than Traditional Juvenile Court (TJC) settings, drawing on the Risk-Need-Responsivity framework and Disproportionate Minority Contact. Using longitudinal secondary data from 415 youth across 10 U.S. jurisdictions, machine learning models (random forests and logistic regression) assess predictors of rearrest up to one year post-intake. Race is not a key feature in final models, while family ineffectiveness, social risk, and crime/violence screening rise as influential risk factors; not receiving an assessed risk level and Hispanic ethnicity relate to lower rearrest likelihood.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages213

Reading Time8 h 57 min

AccessFree to download

Abstract

Dedication

Acknowledgments

List of Tables

List of Figures

Chapter 1: Introduction to Dissertation Topic

Chapter 2: Literature Review

University of Nevada, Reno Risk, Need, and Racial Inequality: A Machine Learning Analysis of Rearrest in Juvenile Drug Treatment Courts and Traditional Juvenile Courts A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Social Psychology by Courtney E. Lyons, M.A. Dr. William Evans/Dissertation Co-Advisor Dr. Shawn Marsh/Dissertation Co-Advisor December 2022 Copyright by Courtney E. Lyons 2022 All Rights Reserved | |
| | --- | --- | i Abstract Juvenile justice system involvement has many impacts on the lives of youth. This often includes negative outcomes for youth who receive highly punitive treatment rather than more rehabilitative approaches. One approach to reforming the juvenile justice system to be rehabilitative is the use of diversion options, such as Juvenile Drug Treatment Courts (JDTCs) . JDTCs are intended to offer more personalized interventions for youth based on their risk and need factors as compared to Tradition Juvenile Court (TJC) settings. To better understand the complex interactions of tailored programming and individual factors for justice-involved youth, an integrated theoretical approach, including the Risk-Need-Responsivity framework and Disproportionate Minority Contact, was used to frame the current study. This study applied machine learning analysis techniques (random forests and logistic regression models) to a rigorous, longitudinal secondary dataset of youth in JDTCs and TJCs to determine which risk and protective factors were most important in predicting rearrest up to 1 year following court intake. The sample included 415 youth from JDTCs and TJCs in 10 jurisdictions across the US. Results revealed that both random forest and logistic regression models performed similarly for each court type as well as the combined sample, and that models were most accurate for the JDTC sample and least accurate for the TJC sample. Highly influential risk factors associated with higher likelihood of having at least one rearrest during the study period included higher scores on the family ineffectiveness scale, social risk scale, and crime and violence screener. Alternatively, highly influential protective factors associated with higher likelihood of not having any rearrests during the study ii period included not having an assessed risk level assigned to youth and being of Hispanic ethnicity. Race and previous juvenile justice system involvement were not important features in preliminary models and therefore were excluded from final models. Implications for future research, data-driven decision-making practices, and the ethics surrounding the use of machine learning models for juvenile justice involved youth are discussed. iii Dedication This dissertation is dedicated to my amazing family support system. To my parents, Clint and Ginger Lyons, for always teaching me that ifI believed in something, I could do it. To brother, Hunter Lyons, for encouraging me to push myself (and setting a high bar to compete against). To Dylan Kelley, for making me smile when I’m really struggling and making me feel like I can do anything. And finally, to Burrito, my best friend who gave me strength to be brave even when things get hard, and Losh, who is always down for a cuddle and a snack. Thank you all for holding me up and helping me be my best self. iv Acknowledgments First, I would like to thank my co-chairs Dr. William Evans and Dr. Shawn Marsh, as well as my committee members, Dr. Emily Hand, Dr. Emily Berthelot, and Dr. Logan Yelderman, who helped me turn this idea into a reality through all of your thoughtful feedback, support, and guidance. Additionally, those who helped me find this dataset and gain access to it, including Katie Mitchell and Paul Bowen, as well as Kathryn Modisette and Dr. Michael Dennis form Chestnut Health Systems. Without your help and generosity, I would not have been able to conduct this study

FAQs

What is the main goal of this dissertation?

To determine how tailored factors used in Juvenile Drug Treatment Courts relate to rearrest outcomes compared with Traditional Juvenile Courts, using machine learning to identify the most predictive risk and protective factors.

Which machine learning methods are used to predict rearrest?

Random forests and logistic regression models are applied to longitudinal secondary data to predict whether youth are rearrested within one year of court intake.

Do race-related variables meaningfully affect the final predictive models?

No. Race and previous juvenile justice system involvement are not important features in preliminary models and are excluded from final models, while specific risk and protective factors remain influential.

Aug 5, 2026

Upload by Jiven