Predictors of substance abuse treatment outcomes in Tennessee.

In planning and implementing programs to treat substance abuse, it is important to understand which factors influence post-treatment abstinence.This article identifies and analyzes several variables important in predicting the likelihood of abstinence among substance abuse clients. The data used in...

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Publicado en:Journal of Drug Education Vol. 33; no. 1; pp. 25 - 48
Autores principales: Kedia S, Williams C
Formato: research tables/charts Journal Article
Publicado: Sage Publications Inc. 2003
Acceso en línea:Ver este registro en EBSCOhost
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      pub: Sage Publications Inc.
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        atl: Predictors of substance abuse treatment outcomes in Tennessee.
      aug:
        au:
          Kedia S
          Williams C
        affil: Assistant Professor of Medical Anthropology, Dept of Anthropology, University of Memphis, 316 Manning Hall, Memphis, TN 38152; skkedia@memphis.edu
      sug:
        subj:
          Drug Rehabilitation Programs Tennessee
          Outcomes (Health Care) Tennessee
          Adolescence
          Adult
          Analysis of Variance
          Chi Square Test
          Confidence Intervals
          Convenience Sample
          Data Analysis Software
          Discriminant Analysis
          Interviews
          Logistic Regression
          Middle Age
          Odds Ratio
          Pearson's Correlation Coefficient
          Pretest-Posttest Design
          Questionnaires
          Telephone
          Tennessee
          Funding Source
          Human
          Adolescent: 13-18 years
          Adult: 19-44 years
          Middle Aged: 45-64 years
      ab: In planning and implementing programs to treat substance abuse, it is important to understand which factors influence post-treatment abstinence.This article identifies and analyzes several variables important in predicting the likelihood of abstinence among substance abuse clients. The data used in this study was collected from 1,350 clients treated for alcohol or drug abuse in residential, halfway house, or outpatient facilities in Tennessee. We analyzed 22 variables as possible treatment outcome predictors by using two statistical procedures: stepwise logistic regression analysis and Quick, Unbiased, Efficient, Statistical Tree (QUEST) analysis, a tree-structured classification algorithm analysis. We found one pretreatment, five in-treatment, and three post-treatment variables to be significant predictors of treatment outcome: previous treatment history, perceived helpfulness of the treatment, simultaneous treatment for mental health, number of days in treatment,completion of treatment, special skills training during treatment, obtaining healthcare services for major physical health problem after treatment, living with someone using alcohol or drugs post treatment, and arrest record since treatment.
      pubtype: Academic Journal
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        tables/charts
        Journal Article
      ougenre: Article
    language: English
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