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• W2339315248 abstract "The dissemination and evaluation of evidence-based behavioral treatments for substance abuse problems rely on the evaluation of counselor interventions. In Motivational Interviewing (MI), a treatment that directs the therapist to utilize a particular linguistic style, proficiency is assessed via behavioral coding-a time consuming, nontechnological approach. Natural language processing techniques have the potential to scale up the evaluation of behavioral treatments such as MI. We present a novel computational approach to assessing components of MI, focusing on 1 specific counselor behavior-reflections, which are believed to be a critical MI ingredient. Using 57 sessions from 3 MI clinical trials, we automatically detected counselor reflections in a maximum entropy Markov modeling framework using the raw linguistic data derived from session transcripts. We achieved 93% recall, 90% specificity, and 73% precision. Results provide insight into the linguistic information used by coders to make ratings and demonstrate the feasibility of new computational approaches to scaling up the evaluation of behavioral treatments." @default.
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• W2339315248 date "2016-04-01" @default.
• W2339315248 modified "2023-10-13" @default.
• W2339315248 title "“It sounds like...”: A natural language processing approach to detecting counselor reflections in motivational interviewing." @default.
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• W2339315248 doi "https://doi.org/10.1037/cou0000111" @default.
• W2339315248 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4833560" @default.
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