Literature DB >> 34537885

Knowledge and Attitudes Toward an Artificial Intelligence-Based Fidelity Measurement in Community Cognitive Behavioral Therapy Supervision.

Torrey A Creed1,2, Patty B Kuo3, Rebecca Oziel4,5, Danielle Reich4,5, Margaret Thomas6, Sydne O'Connor4,5, Zac E Imel3, Tad Hirsch6, Shrikanth Narayanan7, David C Atkins8.   

Abstract

To capitalize on investments in evidence-based practices, technology is needed to scale up fidelity assessment and supervision. Stakeholder feedback may facilitate adoption of such tools. This evaluation gathered stakeholder feedback and preferences to explore whether it would be fundamentally feasible or possible to implement an automated fidelity-scoring supervision tool in community mental health settings. A partially mixed, sequential research method design was used including focus group discussions with community mental health therapists (n = 18) and clinical leadership (n = 12) to explore typical supervision practices, followed by discussion of an automated fidelity feedback tool embedded in a cloud-based supervision platform. Interpretation of qualitative findings was enhanced through quantitative measures of participants' use of technology and perceptions of acceptability, appropriateness, and feasibility of the tool. Initial perceptions of acceptability, appropriateness, and feasibility of automated fidelity tools were positive and increased after introduction of an automated tool. Standard supervision was described as collaboratively guided and focused on clinical content, self-care, and documentation. Participants highlighted the tool's utility for supervision, training, and professional growth, but questioned its ability to evaluate rapport, cultural responsiveness, and non-verbal communication. Concerns were raised about privacy and the impact of low scores on therapist confidence. Desired features included intervention labeling and transparency about how scores related to session content. Opportunities for asynchronous, remote, and targeted supervision were particularly valued. Stakeholder feedback suggests that automated fidelity measurement could augment supervision practices. Future research should examine the relations among use of such supervision tools, clinician skill, and client outcomes.
© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Artificial intelligence; Cognitive behavioral therapy; Community mental health; Fidelity; Machine learning; Supervision; Technology

Mesh:

Year:  2021        PMID: 34537885      PMCID: PMC8930782          DOI: 10.1007/s10488-021-01167-x

Source DB:  PubMed          Journal:  Adm Policy Ment Health        ISSN: 0894-587X


  20 in total

1.  Using client feedback to improve couple therapy outcomes: a randomized clinical trial in a naturalistic setting.

Authors:  Morten G Anker; Barry L Duncan; Jacqueline A Sparks
Journal:  J Consult Clin Psychol       Date:  2009-08

2.  Beyond the Label: Relationship Between Community Therapists' Self-Report of a Cognitive Behavioral Therapy Orientation and Observed Skills.

Authors:  Torrey A Creed; Courtney Benjamin Wolk; Betsy Feinberg; Arthur C Evans; Aaron T Beck
Journal:  Adm Policy Ment Health       Date:  2016-01

3.  Sustaining motivational interviewing: a meta-analysis of training studies.

Authors:  Craig S Schwalbe; Hans Y Oh; Allen Zweben
Journal:  Addiction       Date:  2014-05-29       Impact factor: 6.526

4.  The Structure of Competence: Evaluating the Factor Structure of the Cognitive Therapy Rating Scale.

Authors:  Simon B Goldberg; Scott A Baldwin; Kritzia Merced; Derek D Caperton; Zac E Imel; David C Atkins; Torrey Creed
Journal:  Behav Ther       Date:  2019-05-24

5.  Design feasibility of an automated, machine-learning based feedback system for motivational interviewing.

Authors:  Zac E Imel; Brian T Pace; Christina S Soma; Michael Tanana; Tad Hirsch; James Gibson; Panayiotis Georgiou; Shrikanth Narayanan; David C Atkins
Journal:  Psychotherapy (Chic)       Date:  2019-04-08

6.  Implementation research in mental health services: an emerging science with conceptual, methodological, and training challenges.

Authors:  Enola K Proctor; John Landsverk; Gregory Aarons; David Chambers; Charles Glisson; Brian Mittman
Journal:  Adm Policy Ment Health       Date:  2008-12-23

7.  Implementation of transdiagnostic cognitive therapy in community behavioral health: The Beck Community Initiative.

Authors:  Sarah A Frankel; Ramaris E German; Torrey A Creed; Kelly L Green; Shari Jager-Hyman; Kristin P Taylor; Abby D Adler; Courtney B Wolk; Shannon W Stirman; Scott H Waltman; Michael A Williston; Rachel Sherrill; Arthur C Evans; Aaron T Beck
Journal:  J Consult Clin Psychol       Date:  2016-07-04

8.  Community-based participatory research and integrated knowledge translation: advancing the co-creation of knowledge.

Authors:  Janet Jull; Audrey Giles; Ian D Graham
Journal:  Implement Sci       Date:  2017-12-19       Impact factor: 7.327

9.  "Rate My Therapist": Automated Detection of Empathy in Drug and Alcohol Counseling via Speech and Language Processing.

Authors:  Bo Xiao; Zac E Imel; Panayiotis G Georgiou; David C Atkins; Shrikanth S Narayanan
Journal:  PLoS One       Date:  2015-12-02       Impact factor: 3.240

10.  Psychometric assessment of three newly developed implementation outcome measures.

Authors:  Bryan J Weiner; Cara C Lewis; Cameo Stanick; Byron J Powell; Caitlin N Dorsey; Alecia S Clary; Marcella H Boynton; Heather Halko
Journal:  Implement Sci       Date:  2017-08-29       Impact factor: 7.327

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  1 in total

1.  Enhancing the quality of cognitive behavioral therapy in community mental health through artificial intelligence generated fidelity feedback (Project AFFECT): a study protocol.

Authors:  Torrey A Creed; Leah Salama; Roisin Slevin; Michael Tanana; Zac Imel; Shrikanth Narayanan; David C Atkins
Journal:  BMC Health Serv Res       Date:  2022-09-20       Impact factor: 2.908

  1 in total

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