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（由申请人提供）：数百万美国人正在接受有问题的酒精使用行为干预。2010年，药物滥用和精神卫生服务管理局（SAMHSA）记录了180多万次药物和酒精问题治疗，其中许多涉及团体或个人心理治疗。提供服务的巨大需求使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。最近的一项动机访谈（MI）的元分析表明，“培训后的支持”-如基于绩效的反馈或辅导-是至关重要的，以保持辅导员的技能培训后。但是，实际 对酒精使用障碍（AUD）和有问题饮酒的基于表现的反馈的实施目前在努力、时间和金钱上是禁止的。有一个关键的需要或技术，以“扩大”基于性能的反馈，以辅导员的AUD和有问题的饮酒。 这种竞争性的更新建立在跨学科研究的基础上，重点是自动评估酒精和物质使用问题的MI保真度。这项合作研究汇集了来自电气工程和统计文本挖掘的语音信号处理专家以及来自计算机科学的自然语言处理专家，以及MI专家培训师和研究人员。我们先前的研究为从语义和声音特征生成MI保真度代码奠定了计算基础，目前的建议将这项工作直接应用于临床。在与犹他州咨询中心（UCC）的大学合作，我们将开发和实施一个临床软件支持工具，辅导员观察员评级专家MI（CORE-MI）。核心-MI系统将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，为辅导员治疗与酒精和物质使用问题作斗争的客户。 本研究将采用混合实施-有效性设计，以实现以下三个目标：1）在UCC诊所实施和校准CORE-MI系统，以提供自动化的、基于绩效的MI反馈; 2）比较CORE-MI系统启动前后咨询师对MI的忠诚度以及客户酒精和物质使用的结果（大约，N = 2，400个会话）;以及3）使用机器学习工具，使用语义和声音数据、MI保真度代码以及来自大约3，000个会话的客户端结果来计算地探索MI的机制。该项目的成功实施将打破对人工判断的依赖，为MI提供基于绩效的反馈，并将大规模扩大培训，监督和提供质量保证的能力。