Dataset Information

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

ABSTRACT: 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 (N = 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 (n = 171) versus a non-RHS (e.g., traditional school: n = 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).

SUBMITTER: Hennessy EA

PROVIDER: S-EPMC6889019 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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

Hennessy Emily A EA Finch Andrew J AJ

Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors 20191114 8

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 ind ...[more]

PMID: 31724415

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Project description:BackgroundThere is huge variability in the way that individuals with tinnitus respond to interventions. These experiential variations, together with a range of associated etiologies, contribute to tinnitus being a highly heterogeneous condition. Despite this heterogeneity, a "one size fits all" approach is taken when making management recommendations. Although there are various management approaches, not all are equally effective. Psychological approaches such as cognitive behavioral therapy have the most evidence base. Managing tinnitus is challenging due to the significant variations in tinnitus experiences and treatment successes. Tailored interventions based on individual tinnitus profiles may improve outcomes. Predictive models of treatment success are, however, lacking.ObjectiveThis study aimed to use exploratory data mining techniques (ie, decision tree models) to identify the variables associated with the treatment success of internet-based cognitive behavioral therapy (ICBT) for tinnitus.MethodsIndividuals (N=228) who underwent ICBT in 3 separate clinical trials were included in this analysis. The primary outcome variable was a reduction of 13 points in tinnitus severity, which was measured by using the Tinnitus Functional Index following the intervention. The predictor variables included demographic characteristics, tinnitus and hearing-related variables, and clinical factors (ie, anxiety, depression, insomnia, hyperacusis, hearing disability, cognitive function, and life satisfaction). Analyses were undertaken by using various exploratory machine learning algorithms to identify the most influencing variables. In total, 6 decision tree models were implemented, namely the classification and regression tree (CART), C5.0, GB, XGBoost, AdaBoost algorithm and random forest models. The Shapley additive explanations framework was applied to the two optimal decision tree models to determine relative predictor importance.ResultsAmong the six decision tree models, the CART (accuracy: mean 70.7%, SD 2.4%; sensitivity: mean 74%, SD 5.5%; specificity: mean 64%, SD 3.7%; area under the receiver operating characteristic curve [AUC]: mean 0.69, SD 0.001) and gradient boosting (accuracy: mean 71.8%, SD 1.5%; sensitivity: mean 78.3%, SD 2.8%; specificity: 58.7%, SD 4.2%; AUC: mean 0.68, SD 0.02) models were found to be the best predictive models. Although the other models had acceptable accuracy (range 56.3%-66.7%) and sensitivity (range 68.6%-77.9%), they all had relatively weak specificity (range 31.1%-50%) and AUCs (range 0.52-0.62). A higher education level was the most influencing factor for ICBT outcomes. The CART decision tree model identified 3 participant groups who had at least an 85% success probability following the undertaking of ICBT.ConclusionsDecision tree models, especially the CART and gradient boosting models, appeared to be promising in predicting ICBT outcomes. Their predictive power may be improved by using larger sample sizes and including a wider range of predictive factors in future studies.

Dataset Information

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

Publications

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

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