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Drug Alcohol Depend. 2014 May 1;138:193-201. doi: 10.1016/j.drugalcdep.2014.02.700. Epub 2014 Mar 12.

Using structural equation modeling to understand prescription stimulant misuse: a test of the Theory of Triadic Influence.

Author information

1
University of California, Berkeley, United States. Electronic address: NBavarian@berkeley.edu.
2
Oregon State University, United States.
3
University of California, Berkeley, United States.
4
Prevention Research Center, United States.

Abstract

OBJECTIVE:

To test a theory-driven model of health behavior to predict the illicit use of prescription stimulants (IUPS) among college students.

PARTICIPANTS:

A probability sample of 554 students from one university located in California (response rate=90.52%).

METHODS:

Students completed a paper-based survey developed with guidance from the Theory of Triadic Influence. We first assessed normality of measures and checked for multicollinearity. A single structural equation model of frequency of IUPS in college was then tested using constructs from the theory's three streams of influence (i.e., intrapersonal, social situation/context, and sociocultural environment) and four levels of causation (i.e., ultimate causes, distal influences, proximal predictors, and immediate precursors).

RESULTS:

Approximately 18% of students reported engaging in IUPS during college, with frequency of use ranging from never to 40 or more times per academic term. The model tested had strong fit and the majority of paths specified within and across streams were significant at the p<0.01 level. Additionally, 46% of the variance in IUPS frequency was explained by the tested model.

CONCLUSIONS:

Results suggest the utility of the TTI as an integrative model of health behavior, specifically in predicting IUPS, and provide insight on the need for multifaceted prevention and intervention efforts.

KEYWORDS:

Health behavior theory; Prescription stimulants; Prevention; Substance use

PMID:
24647369
PMCID:
PMC4063447
DOI:
10.1016/j.drugalcdep.2014.02.700
[Indexed for MEDLINE]
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