Gainey R R, Catalano R F, Haggerty K P, Hoppe M J
Social Development Research Group, School of Social Work, University of Washington, Seattle 98103, USA.
Addict Behav. 1995 Jan-Feb;20(1):117-25. doi: 10.1016/0306-4603(94)00053-2.
Programs for drug abusers are plagued by high rates of dropout. Because of the strong relationship between longer treatment and positive outcome, researchers have begun to study individual and program-specific factors that influence premature termination of treatment. For the most part, these studies have focused on dichotomous measures of dropout or number of sessions attended. In this article, we extend this line of research in two ways. First, we develop and measure a number of indicators of treatment participation based on therapist ratings. Second, we develop a model of treatment participation that employs both individual and program-specific factors. The data show that tremendous variation in participation occurred even among those who attended a majority of sessions, which highlights the importance of obtaining more elaborate measures of treatment participation. The model predicting treatment participation suggests that initiation of heroin use later in life, continued use of marijuana, and behavioral indicators of motivation are the strongest predictors of program participation. Research and practical implications of the findings are discussed.
针对药物滥用者的项目深受高辍学率的困扰。由于较长时间的治疗与积极结果之间存在密切关系,研究人员已开始研究影响治疗提前终止的个体因素和项目特定因素。在很大程度上,这些研究集中在辍学的二分法测量或参加的疗程数量上。在本文中,我们从两个方面扩展了这一研究方向。首先,我们基于治疗师的评分开发并测量了一些治疗参与指标。其次,我们开发了一个治疗参与模型,该模型同时采用个体因素和项目特定因素。数据表明,即使在那些参加了大部分疗程的人当中,参与情况也存在巨大差异,这凸显了获得更详尽的治疗参与测量指标的重要性。预测治疗参与情况的模型表明,晚年开始使用海洛因、持续使用大麻以及动机的行为指标是项目参与的最强预测因素。文中讨论了这些研究结果的研究意义和实际意义。