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Not the same old thing: Establishing the unique contribution of drinking identity as a predictor of alcohol consumption and problems over time.

Kristen P LindgrenJason J RamirezCecilia C OlinClayton Neighbors
Published in: Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors (2016)
Drinking identity-how much individuals view themselves as drinkers-is a promising cognitive factor that predicts problem drinking. Implicit and explicit measures of drinking identity have been developed (the former assesses more reflexive/automatic cognitive processes; the latter more reflective/controlled cognitive processes): each predicts unique variance in alcohol consumption and problems. However, implicit and explicit identity's utility and uniqueness as predictors relative to cognitive factors important for problem drinking screening and intervention has not been evaluated. Thus, the current study evaluated implicit and explicit drinking identity as predictors of consumption and problems over time. Baseline measures of drinking identity, social norms, alcohol expectancies, and drinking motives were evaluated as predictors of consumption and problems (evaluated every 3 months over 2 academic years) in a sample of 506 students (57% female) in their first or second year of college. Results found that baseline identity measures predicted unique variance in consumption and problems over time. Further, when compared to each set of cognitive factors, the identity measures predicted unique variance in consumption and problems over time. Findings were more robust for explicit versus implicit identity and in models that did not control for baseline drinking. Drinking identity appears to be a unique predictor of problem drinking relative to social norms, alcohol expectancies, and drinking motives. Intervention and theory could benefit from including and considering drinking identity. (PsycINFO Database Record
Keyphrases
  • alcohol consumption
  • mental health
  • randomized controlled trial
  • emergency department
  • machine learning
  • electronic health record
  • adverse drug