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中和技术的使用预测了犯罪和物质使用的结果。

Neutralization technique use predicts delinquency and substance use outcomes.

机构信息

Seattle Pacific University, 3307 3rd Ave W, Seattle, WA 98119, United States of America.

Seattle Pacific University, 3307 3rd Ave W, Seattle, WA 98119, United States of America; Cambridge Health Alliance, Harvard Medical School, 1493 Cambridge St, Cambridge, MA 02139, United States of America.

出版信息

J Subst Abuse Treat. 2019 Jul;102:8-15. doi: 10.1016/j.jsat.2019.04.006. Epub 2019 Apr 17.

Abstract

This study was part of a larger research intervention that uses motivational interviewing (MI) as part of an in-school substance use intervention in local high schools in the greater Seattle area. Our aim was to test hypothesized relationships between marijuana use, other delinquent behavior, and neutralization techniques used by participants and determine their impact on the effectiveness of an MI-based substance use intervention. Of the 84 participants that completed Intake assessments, 60% were male and identified as an ethnic minority sample: Caucasian/White = 34%; African American/Black = 16%; Hispanic/Latin@ = 16%; Asian American/Asian = 8%; multiethnic = 8%. Forty-eight students completed Week 8 Follow Up assessments. Substance abuse was measured using the Customary Drinking and Drug Use Record (CDDR; Brown et al., 1998). Delinquency was measured using a revised version of the Late Adolescent Delinquency Scale (LADS; McCartan, 2007). Neutralization technique use was measured using a revised version of the Inventory of Neutralization Techniques (Ball, 1966; Ball, 1973; Mitchell & Dodder, 1983; Shields & Whitehall, 1994). Hierarchical linear regression analyses revealed that alcohol use at intake significantly predicted alcohol use at Week 8 Follow Up, F(1,47) = 8.503, p = .005 and accounted for approximately 16% of the model variance. Adding total neutralization to the model also yielded significant outcomes and accounted for 24% of the total variance, FΔ(2,46) = 4.835, p = .033. Data showed similar findings for marijuana, with Intake marijuana use predicting use at Week 8 Follow Up, F(1,47) = 9.542, p = .003, and accounting for approximately 18% of the model variance. Including total neutralization to this model also provided significant outcomes and accounted for 26% of the variance, FΔ(2,46) = 4.611, p = .037. Including delinquency did not significantly contribute to either regression model. Understanding these participants' cognitive strategies will be valuable in determining the most effective type of substance use treatment for each student.

摘要

本研究是一项更大规模研究干预的一部分,该研究采用动机性访谈(MI)作为大西雅图地区当地高中校内药物使用干预的一部分。我们的目的是检验假设的大麻使用、其他犯罪行为与参与者使用的中和技术之间的关系,并确定它们对基于 MI 的药物使用干预有效性的影响。在完成入组评估的 84 名参与者中,有 60%为男性,且属于少数族裔样本:白种人/白人=34%;非裔美国人/黑人=16%;西班牙裔/拉丁裔=16%;亚裔美国人/亚洲人=8%;多种族裔=8%。48 名学生完成了第 8 周随访评估。药物滥用使用定制的饮酒和药物使用记录(CDDR;Brown 等人,1998 年)进行测量。犯罪行为使用经过修订的晚期青少年犯罪量表(LADS;Mcartan,2007 年)进行测量。中和技术的使用使用经过修订的中和技术清单(Ball,1966 年;Ball,1973 年;Mitchell 和 Dodder,1983 年;Shields 和 Whitehall,1994 年)进行测量。分层线性回归分析显示,入组时的酒精使用量显著预测了第 8 周随访时的酒精使用量,F(1,47)=8.503,p=0.005,占模型方差的 16%左右。向模型中添加总中和技术也产生了显著的结果,占总方差的 24%,FΔ(2,46)=4.835,p=0.033。对于大麻的数据也显示出类似的发现,入组时的大麻使用量预测了第 8 周随访时的使用量,F(1,47)=9.542,p=0.003,占模型方差的 18%左右。向该模型中添加总中和技术也提供了显著的结果,占方差的 26%,FΔ(2,46)=4.611,p=0.037。将犯罪行为纳入这两个回归模型并没有显著贡献。了解这些参与者的认知策略对于确定针对每个学生最有效的药物使用治疗类型将是有价值的。

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