Implementation of Technology-Based Evaluation of Motivational Interviewing

基于技术的动机访谈评估的实施

• 批准号: 9334680
• 负责人: David Charles Atkins
• 金额: $ 64.43万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9334680
• 关键词: Acoustics; Addictive Behavior; Alcohol abuse; Alcohol consumption; Alcohol or Other Drugs use; Alcohols; American; Arousal; Assessment tool; Behavior; Behavior Therapy; Behavioral; Calibration; Cause of Death; Client; Clinic; Clinical; Clinical effectiveness; Code; Collaborations; Computer Simulation; Computer software; Computers; Counseling; Data; Development; Educational workshop; Effectiveness; Electrical Engineering; Empathy; Evaluation; Evidence based intervention; Feedback; Foundations; Funding; Group Psychotherapy; Homicide; Human; Hybrids; Individual; Interdisciplinary Study; Intervention Studies; Judgment; Learning; Learning Skill; Life; Linguistics; Machine Learning; Mental Health; Meta-Analysis; Methods; National Institute on Alcohol Abuse and Alcoholism; Natural Language Processing; Outcome; Patient Outcomes Assessments; Patients; Performance; Pharmaceutical Preparations; Phase; Play; Population; Process; Professional counselor; Psychologist; Psychotherapy; Reporting; Research; Research Infrastructure; Research Personnel; Risk; Role; Scientist; Semantics; Services; Software Tools; Speech; Standardization; Substance Use Disorder; Suicide; Supervision; System; Technology; Testing; Time; Training; Training Support; United States Department of Veterans Affairs; United States National Institutes of Health; United States Substance Abuse and Mental Health Services Administration; Universities; Utah; Vision; Work; addiction; alcohol abuse therapy; alcohol intervention; alcohol related problem; alcohol use disorder; base; clinical application; computer science; cost; design; drinking; high risk drinking; improved; motivational enhancement therapy; public health relevance; quality assurance; scale up; signal processing; skills; support tools; technological innovation; technology validation; text searching; tool; vehicular accident; visual feedback; young adult

 DESCRIPTION (provided by applicant): Millions of Americans are receiving behavioral interventions for problematic alcohol use. In 2010, the Substance Abuse and Mental Health Services Administration (SAMHSA) documented over 1.8 million treatment episodes for drug and alcohol problems, many involving group or individual psychotherapy. The tremendous service-delivery need has focused research on optimal training methods, to promote the dissemination of evidence-based interventions. A recent meta-analysis of motivational interviewing (MI) shows that "post-training supports" - such as performance-based feedback or coaching - are critical for maintaining counselor skills following training. However, the practical implementation of performance-based feedback for alcohol use disorders (AUDs) and problematic drinking is currently prohibitive in effort, time, and money. There is a critical need or technology to "scale up" performance-based feedback to counselors for AUDs and problematic drinking. This competitive renewal builds on interdisciplinary research focused on automating the evaluation of MI fidelity for alcohol and substance use problems. This collaborative research brings together speech signal processing experts from electrical engineering and statistical text-mining and natural language processing experts from computer science with MI expert trainers and researchers. Our previous research laid a computational foundation for generating MI fidelity codes from semantic and vocal features, and the current proposal moves this work into direct clinical application. In collaboration with the University of Utah Counseling Center (UCC), we will develop and implement a clinical software support tool, the Counselor Observer Ratings Expert for MI (CORE-MI). The CORE-MI system will provide performance-based feedback focused on MI fidelity codes for training, supervision, and quality assurance for counselors treating clients struggling with alcohol and substance use problems. The research will use a hybrid implementation-effectiveness design to pursue the following three aims: 1) Implement and calibrate the CORE-MI system at the UCC clinic to provide automated, performance-based feedback on MI; 2) Compare counselor fidelity to MI and client alcohol and substance use outcomes, before and after initiation of the CORE-MI system (approximately, N = 2,400 sessions); and 3) Using machine learning tools, computationally explore mechanisms of MI using semantic and vocal data, MI fidelity codes, and client outcomes from approximately 3,000 sessions. The successful execution of this project will break the reliance on human judgment for providing performance-based feedback to MI and will massively expand the capacity to train, supervise, and provide quality assurance.

 描述(由申请者提供)：数百万美国人正在接受有问题的酒精使用的行为干预。2010年，药物滥用和精神卫生服务管理局(SAMHSA)记录了超过180万次药物和酒精问题的治疗事件，其中许多涉及集体或个人心理治疗。巨大的服务提供需求将研究重点放在最佳培训方法上，以促进循证干预措施的传播。最近对激励性面试(MI)的一项元分析显示，“培训后的支持”--例如基于绩效的反馈或指导--对于在培训后保持辅导员技能至关重要。然而，实际情况是 对酒精使用障碍(AUD)和有问题的饮酒实施基于绩效的反馈目前在精力、时间和金钱上都是令人望而却步的。对于AUDS和有问题的饮酒问题，有一种迫切的需求或技术来“扩大”基于绩效的反馈给咨询师。这种竞争性的更新建立在跨学科研究的基础上，专注于自动化酒精和药物使用问题的MI保真度评估。这项合作研究将来自电气工程和统计文本挖掘的语音信号处理专家和来自计算机科学的自然语言处理专家与MI专家培训人员和研究人员聚集在一起。我们之前的研究为从语义和语音特征生成MI保真码奠定了计算基础，目前的建议将这项工作直接应用于临床。我们将与犹他大学咨询中心(UCC)合作，开发和实施临床软件支持工具-MI的顾问观察员评级专家(CORE-MI)。CORE-MI系统将为治疗酗酒和药物使用问题的客户提供基于绩效的反馈，重点是MI保真度代码，用于培训、监督和质量保证。这项研究将使用混合实施-有效性设计来追求以下三个目标：1)在UCC诊所实施和校准CORE-MI系统，以提供关于MI的自动化、基于绩效的反馈；2)比较咨询师对MI以及客户酒精和药物使用结果的忠诚度，在CORE-MI系统启动前后(大约N=2,400次会议)；以及3)使用机器学习工具，使用语义和语音数据、MI保真度代码和大约3,000次会议的客户结果，通过计算探索MI的机制。该项目的成功实施将打破对人的判断的依赖，向MI提供基于业绩的反馈，并将极大地扩展培训、监督和提供质量保证的能力。