Predicting adolescent problem use of marijuana: development and testing of a Bayesian model.
This paper reports on the development and testing of a risk assessment index for problem marijuana use designed to guide teenagers through an extensive computer-based support system intended to help them improve marijuana-related behaviors. Bayesian decision theory, used as the basis of the index development process, offers the advantage of building the index on subjective judgments of experts and does not require a large empirical data base. The index was found to predict an independent panel's ratings of teenager risk, and predict the marijuana use of 10th graders using self-reports of their profiles in the 7th grade. Implications for future risk assessment developments are discussed.
Gustafson
D H
DH
Department of Industrial Engineering, University of Wisconsin-Madison.
Bosworth
K
K
Treece
C
C
Wu
Y C
YC
Palmer
C G
CG
Moberg
D P
DP
Hawkins
R P
RP
D000293:Adolescent; D000328:Adult; D001499:Bayes Theorem; D002648:Child; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D017144:Focus Groups; D006801:Humans; D007600:Judgment; D002189:Marijuana Abuse / Q000517:prevention & control* / Q000523:psychology; D011237:Predictive Value of Tests; D011569:Psychiatric Status Rating Scales; D012015:Reference Standards; D015203:Reproducibility of Results; D012307:Risk Factors