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Objective: Previous research has indicated high rates of intercity movement among homeless young adults (HYAs) for a variety of prosocial (e.g., avoiding domestic violence and seeking new employment opportunities) and antisocial (e.g., following drug supplies and avoiding law enforcement) reasons. The complicated mixture of individual circumstances associated with transience has made it difficult to predict features of transience, such as distance traveled and move frequency. Method: This study describes a method to build an artificial neural network (ANN) that predicts distance traveled and compares the results of thatANN to a generalized linear regression. Results: The ANN more accurately predicts distance traveled than does the linear statistical model and advances the development of approaches to predict complicated human phenomena. Conclusions: Accurately predicting features of transience among HYAs is important in tailoring effective interventions aimed at minimizing travel for negative reasons and making travel for positive reasons safer. [ABSTRACT FROM AUTHOR]

Methamphetamine use among homeless youths is an increasing problem. School officials and social work practitioners are presented with a classification problem in determining which youth are or are not using methamphetamines. The purpose of this study is to adopt a machine-learning approach to address this type of classification problem and to compare 2 models (decision rules and logistic regression) for classifying cases into methamphetamine users and nonusers. The selection of predictors in our models was guided by the risk and resilience framework. Logistic regression and decision rules analyses are used to test the models with a subset of data for 2,146 homeless youth who attend high school that was obtained from the 2007-08 California Healthy Kids Survey dataset. Results of logistic regression suggest methamphetamine use is associated with cigarette and marijuana use, having consumed alcohol, and being truant more than once per week. Results of decision rules analysis suggest a youth's being classified as a methamphetamine user depends on whether the youth has tried marijuana at least once and whether the youth has been truant more than once per week. Moreover, classification as a methamphetamine user also depends on whether a youth has tried marijuana at least once, has not been truant more than once per week, and has tried cigarettes at least once. The logistic regression and decision rules models produce similar--but not identical--results. Our findings highlight the utility of decision rules models as a complement to logistic regression when classification is the goal of a study. Such models can be used to guide social work practice decisions in making informed predictions about client outcomes. [ABSTRACT FROM AUTHOR]

Homeless, street-dwelling youths are an at-risk population who often use survival behaviors to meet their basic needs. The traditional outreach approach brings services into the streets, yet does not adequately replace the youths' high-risk behaviors. Similarly, job training programs often fail to address the mental health issues that constitute barriers to their productive employment. Drawing on social development principles, the Social Enterprise Intervention (SEI) model is proposed as an alternative approach. The SEI seeks the tripartite effect of employment, service-related, and mental health outcomes for street youths. This article compares existing intervention models and suggests that through the SEI, homeless youths can acquire vocational and business skills, clinical mentorship, and linkages to services that otherwise would not be available to them, given their street-dwelling status. [ABSTRACT FROM AUTHOR]