Predictive modeling of service discontinuation in transitional age youth with recent behavioral health service use

Readiness to change among justice-involved young adults in an alternative sentencing program who screened positive for alcohol or drug risk

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Justice-involved young adults are in need of tailored substance use interventions.

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Readiness to change is a key component of substance use behavioral change.

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Many justice-involved young adults were ready to change their substance use.

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Personal reasons to quit using drugs are a potential intervention target.

Introduction

Readiness to change is a key component of substance use behavioral change; yet little is known about readiness to change among justice-involved young adults. This study 1) describes readiness to change alcohol and drug use and 2) examines predictors of readiness to change alcohol and drug use among justice-involved young adults.

Method

Justice-involved young adults (18–24 years; n = 137) who were positive on a validated alcohol and/or drug screening tool completed an interview assessing substance use, readiness to change, and reasons to quit. A multivariable linear regression model examined whether reasons to change and substance use severity, and interactions between these, predicted readiness.

Results

More than half of participants were contemplating or had decided to quit/cut down substance use. Personal reasons to quit were positively related to readiness to change; interpersonal reasons were negatively associated.

Conclusions

This study contributes information needed to design motivational interventions for substance use among justice-involved young adults. Personal reasons to quit using drugs are a potential intervention target.

Deep Learning-Based Mental Health Model on Primary and Secondary School Students’ Quality Cultivation

The purpose was to timely identify the mental disorders (MDs) of students receiving primary and secondary education (PSE) (PSE students) and improve their mental quality. Firstly, this work analyzes the research status of the mental health model (MHM) and the main contents of PSE student-oriented mental health quality cultivation under deep learning (DL). Secondly, an MHM is implemented based on big data technology (BDT) and the convolutional neural network (CNN). Simultaneously, the long short-term memory (LSTM) is introduced to optimize the proposed MHM. Finally, the performance of the MHM before and after optimization is evaluated, and the PSE student-oriented mental health quality training strategy based on the proposed MHM is offered. The results show that the accuracy curve is higher than the recall curve in all classification algorithms. The maximum recall rate is 0.58, and the minimum accuracy rate is 0.62. The decision tree (DT) algorithm has the best comprehensive performance among the five different classification algorithms, with accuracy of 0.68, recall rate of 0.58, and F1-measure of 0.69. Thus, the DT algorithm is selected as the classifier. The proposed MHM can identify 56% of students with MDs before optimization. After optimization, the accuracy is improved by 0.03. The recall rate is improved by 0.19, the F1-measure is improved by 0.05, and 75% of students with MDs can be identified. Diverse behavior data can improve the recognition effect of students' MDs. Meanwhile, from the 60th iteration, the mode accuracy and loss tend to be stable. By comparison, batch_size has little influence on the experimental results. The number of convolution kernels of the first convolution layer has little influence. The proposed MHM based on DL and CNN will indirectly improve the mental health quality of PSE students. The research provides a reference for cultivating the mental health quality of PSE students.