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Medications are typically administered to achieve therapeutic effects. Some drugs can modify an individual's mood and perception, frequently resulting in various enjoyable experiences. However, this can result in drug dependency, a condition marked by continuous drug use despite potential negative consequences. Drug dependency primarily falls into two categories: psychological and physical dependence. Psychological dependence occurs when the pleasurable feelings induced by the drug...
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Predicting Adolescent Substance Use in a Child Welfare Sample: A Multi-Indicator Algorithm.

Suvarna V Menon1, Hena Thakur1, Ryan C Shorey2

  • 1University of Illinois at Urbana Champaign, Champaign, IL, USA.

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|October 12, 2019
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Summary
This summary is machine-generated.

Identifying adolescents at risk for substance use (SU) is crucial. Routine assessments, including the CRAFFT tool, can predict future SU in child welfare populations, aiding early intervention efforts.

Keywords:
adolescencechild welfarescreeningsubstance use

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Area of Science:

  • Public Health
  • Adolescent Psychology
  • Substance Use Research

Background:

  • Adolescents in the child welfare system face elevated risks for substance use (SU).
  • Early identification of impairing SU behaviors is a critical public health objective.
  • Existing assessments may offer predictive value for SU risk in this vulnerable group.

Purpose of the Study:

  • To assess the incremental validity of routine psychological distress measures and SU screening tools.
  • To predict future substance use (SU) at 18 and 36 months post-baseline in a child welfare sample.
  • To develop predictive algorithms for forecasting SU risk in adolescents.

Main Methods:

  • Utilized a nationally representative sample of 1,054 adolescents (mean age 13.72) in the child welfare system.
  • Employed receiver operator characteristic (ROC) and reclassification analyses to develop prediction models.
  • Included baseline Child Behavior Checklist, Youth Self-Report, CRAFFT scores, delinquent behavior, and rule-breaking behavior.

Main Results:

  • A model combining CRAFFT scores, self-reported delinquent behavior, and parent-reported rule-breaking predicted SU in females.
  • CRAFFT scores and self-reported delinquent behavior predicted SU in males.
  • These assessments demonstrated incrementally valid prediction for SU behavior in both genders.

Conclusions:

  • Existing child welfare system assessments can be leveraged to improve SU risk forecasting.
  • Tailored prediction models based on routine data can enhance identification of at-risk adolescents.
  • Findings support the integration of these tools for proactive SU prevention strategies.