Identifying Effective Motivational Interviewing Communication Sequences Using Automated Pattern Analysis.
Identifying Effective Motivational Interviewing Communication Sequences Using Automated Pattern Analysis.
复制标题
使用自动模式分析识别有效的动机访谈沟通序列。
DOI：
10.1007/s41666-018-0037-6
复制
发表时间：
2019
影响因子：
5.9
通讯作者：
Kotov,Alexander
中科院分区：
文献类型：
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作者：
Hasan,Mehedi;Carcone,AprilIdalski;Naar,Sylvie;Eggly,Susan;Alexander,GwenL;Hartlieb,KathrynEBrogan;Kotov,Alexander
动机访谈（MI）是一种基于证据的策略，用于与患者沟通行为改变。尽管有强有力的经验证据将“MI一致”的咨询师行为和患者的动机陈述联系起来（即，“改变谈话”），有效地引起患者改变谈话的特定咨询师沟通行为因治疗环境而异，因此是正在进行的研究的主题。本研究的一个组成部分是预编码MI成绩单的顺序分析。在本文中，我们评估的经验有效性的隐马尔可夫模型，概率生成模型的序列数据，建模序列的行为代码和封闭的频繁模式挖掘，一种方法来识别频繁发生的顺序模式的行为代码在MI通信序列，以告知MI的做法。我们对来自37个转录的减肥咨询会议录音的1，360个通信序列进行了实验，这些会议是与患有肥胖症的非洲裔美国青少年及其照顾者进行的。成绩单先前已使用专门的密码本与患者咨询师行为代码进行了注释。实证结果表明，隐马尔可夫模型和封闭频繁模式挖掘技术可以识别咨询师的沟通策略，是有效地引发病人的动机陈述，以指导临床实践。
Motivational interviewing (MI) is an evidence-based strategy for communicating with patients about behavior change. Although there is strong empirical evidence linking “MI-consistent” counselor behaviors and patient motivational statements (i.e., “change talk”), the specific counselor communication behaviors effective for eliciting patient change talk vary by treatment context and, thus, are a subject of ongoing research. An integral part of this research is the sequential analysis of pre-coded MI transcripts. In this paper, we evaluate the empirical effectiveness of the Hidden Markov Model, a probabilistic generative model for sequence data, for modeling sequences of behavior codes and closed frequent pattern mining, a method to identify frequently occurring sequential patterns of behavior codes in MI communication sequences to inform MI practice. We conducted experiments with 1,360 communication sequences from 37 transcribed audio recordings of weight loss counseling sessions with African-American adolescents with obesity and their caregivers. Transcripts had been previously annotated with patient-counselor behavior codes using a specialized codebook. Empirical results indicate that Hidden Markov Model and closed frequent pattern mining techniques can identify counselor communication strategies that are effective at eliciting patients’ motivational statements to guide clinical practice.