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预测青少年使用大麻的情况。

Predicting marijuana use among adolescents.

作者信息

Vulcano B A, Barnes G E, Langstaff P

机构信息

Department of Psychology, Saint Mary's University, Halifax, Nova Scotia, Canada.

出版信息

Int J Addict. 1990 May;25(5):531-44. doi: 10.3109/10826089009053174.

DOI:10.3109/10826089009053174
PMID:2258259
Abstract

The present paper (1) contrasts the prevalence of marijuana use and involvement with marijuana among 194 delinquent and 405 nondelinquent adolescents, (2) examines the utility of an expanded version of Jessor and Jessor's (1977) problem behavior model in predicting adolescent marijuana use, and (3) tests the relative importance of the predictors of marijuana use. Personality variables that were added to the Jessor problem behavior model included: (1) stimulus reducing - augmenting, (2) ego strength, (3) anxiety, and (4) field dependence. Results showed that delinquents reported using marijuana more often than nondelinquents. In the multiple regression analyses the expanded model explained a slightly greater percentage of the variance in adolescent marijuana use than the Jessor and Jessor model. Of the added personality variables, the Vando (1969) Reducer--Augmenter dimension seemed to be a particularly significant predictor. In addition, reducing--augmenting seemed to be a better predictor of involvement with marijuana than several previously used personality and demographic variables since it replaced these in the final regression equation. Perceived environment variables measuring pressure from friends to use marijuana and friends as models for marijuana use were the best predictors of marijuana use.

摘要

本文(1)对比了194名有犯罪行为的青少年和405名无犯罪行为的青少年中使用大麻及卷入大麻相关活动的比例,(2)检验了杰索尔和杰索尔(1977)问题行为模型的扩展版本在预测青少年使用大麻方面的效用,(3)测试了大麻使用预测因素的相对重要性。添加到杰索尔问题行为模型中的人格变量包括:(1)刺激减弱 - 增强,(2)自我强度,(3)焦虑,以及(4)场依存性。结果显示,有犯罪行为的青少年报告使用大麻的频率高于无犯罪行为的青少年。在多元回归分析中,扩展模型解释青少年使用大麻的方差比例比杰索尔模型略高。在添加的人格变量中,万多(1969)的减弱 - 增强维度似乎是一个特别显著的预测因素。此外,减弱 - 增强似乎比之前使用的几个个性和人口统计学变量更能预测卷入大麻相关活动,因为它在最终回归方程中取代了这些变量。测量来自朋友使用大麻的压力以及朋友作为使用大麻榜样的感知环境变量是大麻使用的最佳预测因素。

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