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（应用程序提供）：数百万美国人正在接受有问题的饮酒行为干预措施。 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，其中许多涉及组或个人心理治疗。巨大的服务递送需求重点研究了最佳培训方法，以促进基于证据的干预措施的传播。最近对动机访谈（MI）的荟萃分析表明，“培训后支持”（例如基于绩效的反馈或教练）对于在培训后维持辅导员技能至关重要。但是，实用目前，努力，时间和金钱实施基于绩效的反馈（AUDS）和有问题的饮酒。有一个迫切需要或技术来“扩展”基于绩效的反馈，以符合声音和有问题的饮酒。这种竞争性更新建立在跨学科研究的基础上，重点是自动化对酒精和药物使用问题的MI保真度的评估。这项合作研究将来自电气工程的语音信号处理专家以及来自计算机科学的统计文本挖掘和自然语言处理专家与MI专家培训师和研究人员一起。我们以前的研究为从语义和声音特征生成MI Fidelity代码的计算基础，当前的提案将这项工作转移到了直接的临床应用中。在与犹他大学咨询中心（UCC）的合作中，我们将开发和实施临床软件支持工具，即MI（Core-MI）的辅导员观察者评级专家。 Core-MI系统将为培训，监督和质量保证提供基于绩效的反馈，以治疗苦苦挣扎的酒精和药物使用问题的客户。该研究将使用混合实施效应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，以提供对MI的自动化，基于性能的反馈； 2）将辅导员的保真度与MI和客户的酒精和物质使用结果进行比较，在启动Core-Mi系统之前和之后（大约为n = 2,400次会议）； 3）使用机器学习工具，使用语义和声音数据，MI Fidelity代码以及大约3,000个会话的客户结果探索MI的机制。该项目的成功执行将打破对人类法官的救济，因为向MI提供了基于绩效的反馈，并将大大扩大培训，监督和提供质量保证的能力。