Implementation of Technology-Based Evaluation of Motivational Interviewing

基于技术的动机访谈评估的实施

基本信息

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
项目类别：
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

项目摘要

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）：数以百万计的美国人正在接受针对酗酒问题的行为干预。2010 年，药物滥用和心理健康服务管理局 (SAMHSA) 记录了超过 180 万起针对毒品和酗酒问题的治疗案例，其中许多涉及团体或个人。巨大的服务提供需求集中于最佳培训方法的研究，以促进基于证据的干预措施的传播。最近的一项动机访谈（MI）荟萃分析表明： “培训后支持”——例如基于绩效的反馈或辅导——对于培训后保持辅导员技能至关重要。目前，针对酒精使用障碍 (AUD) 和酗酒问题实施基于绩效的反馈在精力、时间和金钱方面都令人望而却步，因此迫切需要或技术来“扩大”针对 AUD 和酗酒问题咨询师的基于绩效的反馈。这项竞争性更新建立在跨学科研究的基础上，重点是自动评估酒精和药物使用问题的 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和 MI 专家。培训师和我们之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，当前的提案将与犹他大学咨询中心 (UCC) 合作开发和实施这项工作。临床软件支持工具，心理咨询师观察员评级专家 (CORE-MI) CORE-MI 系统将提供基于绩效的反馈，重点关注 MI 保真度代码，为治疗酗酒客户的咨询师提供培训、监督和质量保证。和物质使用问题。研究将采用混合实施-有效性设计来实现以下三个目标：1）在 UCC 诊所实施和校准 CORE-MI 系统，以提供基于绩效的自动化 MI 反馈；2）比较顾问对 MI 和客户的忠诚度。酒精和物质使用结果，在 CORE-MI 系统启动之前和之后（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 保真度以计算方式探索 MI 机制；该项目的成功执行将打破向 MI 提供基于绩效的反馈对人类判断的依赖，并将大幅扩展培训、监督和提供质量保证的能力。