ABUSE/DISTRESS MODEL OF DRUG TREATMENT ENGAGEMENT

描述： 拟议的研究将确定过去和当前性行为的影响 和身体虐待对客户参与戒毒治疗和治疗的影响 有效性。 它将测试虐待-痛苦 (A-D) 理论模型 预测治疗参与度较低和复发风险较高 对于那些有性虐待和身体虐待历史的人。 研究结果将 添加有关潜在影响的证据记录 创伤经历和合并症对治疗参与、保留的影响 和结果。 此外，它将实证探索 A-D 的实用性 男性和女性、种族群体和不同群体的模型 虐待和治疗史。 最后，它将识别客户端 是治疗参与度的重要预测因素的特征 复发并且可能适合临床干预。 的 分析将利用最近完成和正在进行的五个项目的数据 治疗评估“搭载”基础科学问题 评价问题。 评估涉及回顾性/前瞻性 纵向设计征求预处理、治疗中和 超过 1000 名男性和女性受试者的治疗后客户信息 具有不同的民族和种族背景。 所有五项研究均包括 性虐待和身体虐待的标准化措施以及频繁的 使用评估 A-D 和其他预测变量的措施和标准化量表 变量。 连同 OLS 和逻辑回归、重复测量、 判别分析和其他多元统计应用， 结构方程模型（EQS）将用于测量 A-D 预测变量与治疗之间的相互关系 参与和复发风险。

DESCRIPTION: The proposed research will determine the effects of past and current sexual and physical abuse on client engagement in drug treatment and treatment effectiveness. It will test the Abuse-Distress (A-D) theoretical model that predicts lower levels of treatment engagement and a higher risk of relapse for those with histories of sexual and physical abuse. The findings will add to the evidentiary record concerning the effects of potentially traumatic experiences and comorbidity on treatment participation, retention and outcomes. Moreover, it will empirically explore the utility of the A-D model for males and females, ethnic populations, and those with different abuse and treatment histories. Finally, it will identify client characteristics that are important predictors of treatment engagement and relapse and that are potentially amenable to clinical intervention. The analyses will utilize data from five recently completed and ongoing treatment evaluations that "piggy-back" basic science questions with the evaluation concerns. The evaluations involve retrospective/prospective longitudinal designs soliciting pre-treatment, in-treatment, and post-treatment client information on more than 1000 male and female subjects with differing ethnic and racial backgrounds. All five studies include standardized measures of sexual and physical abuse along with frequently used measures and standardized scales assessing the A-D and other predictor variables. Along with OLS and logistic regressions, repeated measures, discriminant analysis, and other multivariate statistical applications, structural equations modeling (EQS) will be utilized to measure the interrelationship between the A-D predictor variables and treatment engagement and relapse risk.