Adolescent recovery capital and recovery high school attendance: An exploratory data mining approach.

Nov 15, 2019Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors

Resources supporting teen recovery and their attendance at recovery-focused high schools: A data mining study

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Abstract

Only 171 out of 294 adolescents in recovery attended a recovery high school for at least 28 days during a 12-month period.

Attendance at recovery high schools (RHSs) is influenced by individual-level factors such as problem-solving skills and financial resources.
Higher frequency of participation in 12-Step programs is associated with increased odds of attending an RHS.
Significant disparities exist in access to RHSs, suggesting barriers that prevent many youth from utilizing this resource.
The adolescent-adapted recovery capital model (RCAM) effectively identifies predictors of RHS attendance.

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Recovery high schools (RHSs) provide a recovery-supportive academic environment for adolescents in recovery from a substance use disorder and are located across the United States. However, only a small proportion of the 160,000 youth in recovery each year in the United States enroll in RHSs posttreatment, indicating that many youth do not access this relapse prevention resource despite its effectiveness. Thus, this study uses the adolescent-adapted recovery capital model (RCAM) to understand individual- and community-level predictors of attendance and identify disparities leading to barriers to accessing RHSs. Data were collected as part of a multisite observational study of adolescents in recovery (= 294). Logistic regressions and classification trees explored which different recovery capital factors predicted the odds of attending an RHS for at least 28 days during a 12-month period (= 171) versus a non-RHS (e.g., traditional school:= 123). The RCAM model is a useful theoretical framework for examining predictors of RHS attendance, and both analysis methods identified multiple unique predictors of RHS attendance. The strongest predictors of RHS attendance were individual-level factors, including problem-solving skills, 12-Step frequency, and financial resources. The classification trees highlighted additional interactions that should be explored in future empirical research. (PsycINFO Database Record (c) 2019 APA, all rights reserved). N n n

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