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Related Experiment Videos

Predictive validity of Marlatt's relapse taxonomy versus a more general relapse code

R L Stout1, R Longabaugh, A Rubin

  • 1Center for Alcohol and Addiction Studies, Brown University, Providence, Rhode Island, USA.

Addiction (Abingdon, England)
|December 1, 1996
PubMed
Summary

Marlatt's relapse classification system and an alternative coding scheme showed limited predictive validity for drinking outcomes. Repetitive relapse situations often involved social or emotional factors, suggesting their importance in treatment planning.

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Area of Science:

  • Addiction research
  • Psychology
  • Behavioral science

Background:

  • Marlatt's taxonomy is a system for classifying relapse contexts.
  • Potential constraints in Marlatt's taxonomy may limit its validity.
  • The study aimed to compare its predictive validity against a less constrained coding scheme.

Purpose of the Study:

  • To compare the predictive validity of Marlatt's taxonomy with an alternative coding scheme.
  • To assess the ability of these systems to predict drinking outcomes and time to relapse.
  • To identify critical dimensions of relapse situations for treatment planning.

Main Methods:

  • A comparative study involving 300 subjects from six treatment facilities.
  • Assessment of Marlatt's taxonomy and an alternative coding scheme.

Related Experiment Videos

  • Analysis of predictive validity for drinking outcome and time to relapse.

    • Main Results:

    • Marlatt's taxonomy did not significantly predict drinking outcome or time to relapse.
    • Weak evidence suggested Marlatt's taxonomy could predict relapse type in some cases.
    • The alternative system showed potential in identifying repetitive relapse factors like social isolation and negative emotions.

    Conclusions:

    • Both classification systems demonstrated minimal predictive validity for drinking outcomes.
    • Repetitive relapse situations frequently involved lack of social interaction, family context, anxiety, and depression.
    • These identified relapse attributes may be valuable for informing treatment planning strategies.