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Automated evaluation of psychotherapy skills using speech and language technologies.
Flemotomos, Nikolaos; Martinez, Victor R; Chen, Zhuohao; Singla, Karan; Ardulov, Victor; Peri, Raghuveer; Caperton, Derek D; Gibson, James; Tanana, Michael J; Georgiou, Panayiotis; Van Epps, Jake; Lord, Sarah P; Hirsch, Tad; Imel, Zac E; Atkins, David C; Narayanan, Shrikanth.
Afiliação
  • Flemotomos N; Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA. flemotom@usc.edu.
  • Martinez VR; Department of Computer Science, University of Southern California, Los Angeles, CA, 90089, USA.
  • Chen Z; Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA.
  • Singla K; Department of Computer Science, University of Southern California, Los Angeles, CA, 90089, USA.
  • Ardulov V; Department of Computer Science, University of Southern California, Los Angeles, CA, 90089, USA.
  • Peri R; Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA.
  • Caperton DD; Department of Educational Psychology, University of Utah, Salt Lake City, Utah, USA.
  • Gibson J; Behavioral Signal Technologies Inc., Los Angeles, CA, USA.
  • Tanana MJ; College of Social Work, University of Utah, Salt Lake City, Utah, USA.
  • Georgiou P; Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA.
  • Van Epps J; University Counseling Center, University of Utah, Salt Lake City, Utah, USA.
  • Lord SP; Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, Washington, USA.
  • Hirsch T; Department of Art + Design, Northeastern University, Boston, Massachusetts, USA.
  • Imel ZE; Department of Educational Psychology, University of Utah, Salt Lake City, Utah, USA.
  • Atkins DC; Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, Washington, USA.
  • Narayanan S; Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA.
Behav Res Methods ; 54(2): 690-711, 2022 04.
Article em En | MEDLINE | ID: mdl-34346043
With the growing prevalence of psychological interventions, it is vital to have measures which rate the effectiveness of psychological care to assist in training, supervision, and quality assurance of services. Traditionally, quality assessment is addressed by human raters who evaluate recorded sessions along specific dimensions, often codified through constructs relevant to the approach and domain. This is, however, a cost-prohibitive and time-consuming method that leads to poor feasibility and limited use in real-world settings. To facilitate this process, we have developed an automated competency rating tool able to process the raw recorded audio of a session, analyzing who spoke when, what they said, and how the health professional used language to provide therapy. Focusing on a use case of a specific type of psychotherapy called "motivational interviewing", our system gives comprehensive feedback to the therapist, including information about the dynamics of the session (e.g., therapist's vs. client's talking time), low-level psychological language descriptors (e.g., type of questions asked), as well as other high-level behavioral constructs (e.g., the extent to which the therapist understands the clients' perspective). We describe our platform and its performance using a dataset of more than 5000 recordings drawn from its deployment in a real-world clinical setting used to assist training of new therapists. Widespread use of automated psychotherapy rating tools may augment experts' capabilities by providing an avenue for more effective training and skill improvement, eventually leading to more positive clinical outcomes.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Relações Profissional-Paciente / Fala Tipo de estudo: Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Relações Profissional-Paciente / Fala Tipo de estudo: Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article