Evidence map›Paper›PMID 34346043›Full record

ArticleBehavior research methods2022

Automated evaluation of psychotherapy skills using speech and language technologies.

Nikolaos Flemotomos, Victor R Martinez, Zhuohao Chen, Karan Singla, Victor Ardulov, Raghuveer Peri, Derek D Caperton, James Gibson, Michael J Tanana, Panayiotis Georgiou and 6 more

Abstract read
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In one paragraph

Article in Behavior research methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

38 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Article
  5. Targeting mechanisms of change as a path to precision psychotherapy for community settings.Psychotherapy research : journal of the Society for Psychotherapy Research · 2026
    Article
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  7. Article
  8. A Framework for Automation in Psychotherapy.Current directions in psychological science · 2025
    Article
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  19. Machine-Learning-Based Prediction of Client Distress From Session Recordings.Clinical psychological science : a journal of the Association for Psychological Science · 2024
    Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Nikolaos FlemotomosDepartment of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA. flemotom@usc.edu.
Victor R MartinezDepartment of Computer Science, University of Southern California, Los Angeles, CA, 90089, USA.
Zhuohao ChenDepartment of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA.
Karan SinglaDepartment of Computer Science, University of Southern California, Los Angeles, CA, 90089, USA.
Victor ArdulovDepartment of Computer Science, University of Southern California, Los Angeles, CA, 90089, USA.
Raghuveer PeriDepartment of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA.
Derek D CapertonDepartment of Educational Psychology, University of Utah, Salt Lake City, Utah, USA.
James GibsonBehavioral Signal Technologies Inc., Los Angeles, CA, USA.
Michael J TananaCollege of Social Work, University of Utah, Salt Lake City, Utah, USA.
Panayiotis GeorgiouDepartment of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA.
Jake Van EppsUniversity Counseling Center, University of Utah, Salt Lake City, Utah, USA.
Sarah P LordDepartment of Psychiatry and Behavioral Sciences, University of Washington, Seattle, Washington, USA.
Tad HirschDepartment of Art + Design, Northeastern University, Boston, Massachusetts, USA.
Zac E ImelDepartment of Educational Psychology, University of Utah, Salt Lake City, Utah, USA.
David C AtkinsDepartment of Psychiatry and Behavioral Sciences, University of Washington, Seattle, Washington, USA.
Shrikanth NarayananDepartment of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA.

Funding

Implementation of Technology-Based Evaluation of Motivational InterviewingR01AA018673 · NIAAA · UNIVERSITY OF WASHINGTON · PI ATKINS, DAVID CHARLES, NARAYANAN, SHRIKANTH · 2010 to 2020
$5.9M
NIAAA NIH HHS R01 AA018673
6 · The paper itself

Abstract

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.

Indexed as

Professional-Patient RelationsSpeechHumansLanguagePsychotherapyMachine learningMISCMotivational interviewingPsychotherapyQuality assessmentSpeech processing

Identifiers

PMID34346043
PMCPMC8810915

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.