ArticleBMC health services research2022
Enhancing the quality of cognitive behavioral therapy in community mental health through artificial intelligence generated fidelity feedback (Project AFFECT): a study protocol.
Article in BMC health services research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05340738 (Enhancing the Quality of CBT in Community Mental Health Through AI-generated Fidelity Feedback), which is not on this map. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
Enhancing the Quality of CBT in Community Mental Health Through AI-generated Fidelity Feedback
Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.
- Assessing fidelity measurements in school-based anxiety, depression and suicide prevention programs: a systematic review.BMC public health · 2025Pooled it
- Capitalizing on natural language processing (NLP) to automate the evaluation of coach implementation fidelity in guided digital cognitive-behavioral therapy (GdCBT).Psychological medicine · 2025Trial
- Physiotherapists experiences with cognitive behavioural therapy for insomnia (CBT-I) training: Mixed methods study.Osteoarthritis and cartilage open · 2026Article
- Article
- An AI-Driven Virtual Patient Platform (CBT Trainer) for Training Cognitive Behavioral Therapy Practitioners Against Competencies: Mixed Methods Pilot Study.JMIR medical education · 2026Article
- Alignment of Large Language Model Responses With Human Therapists in Motivational Interviewing.JAMA network open · 2026Article
- A review of artificial intelligence enhanced cognitive behavioural therapy using the BECK AI BOT for mental health interventions.Discover mental health · 2026Article
- Treatment outcomes for military-affiliated clients with posttraumatic stress disorder in a community mental health network.Journal of traumatic stress · 2026Article
- Characterizing Fidelity Monitoring Practices in Community Behavioral Health Care.The journal of behavioral health services & research · 2026Article
- A transdiagnostic conflict-square algorithm: a four-node computational framework for psychotherapy and functional diagnosis.Frontiers in psychiatry · 2026Article
- Internet-delivered cognitive behavioral therapy for insomnia: The future of insomnia treatment with large language models.Sleep medicine: X · 2025Review
- Predictors of skill trajectories in the implementation of cognitive behavioral therapy.Implementation science communications · 2025Article
- Performance of Open-Source Large Language Models in Psychiatry: Usability Study Through Comparative Analysis of Non-English Records and English Translations.Journal of medical Internet research · 2025Article
- Prioritizing partnerships in school-based implementation research and practice: Applying the ACCESS model.Evidence-based practice in child and adolescent mental health · 2025Article
- Using machine learning to increase access to and engagement with trauma-focused interventions for posttraumatic stress disorder.The British journal of clinical psychology · 2025Review
- A comparison of scalable routine clinical materials and observer ratings to assess CBT fidelity.Behaviour research and therapy · 2025Observational
- Developing Therapists' Multicultural Orientation Using Web-Based Deliberate Practice: An Initial Feasibility, Usability, and Acceptability Study.Professional psychology, research and practice · 2024Article
- The Future of Psychotherapy Training.Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 5 institutions in 1 country.
Funding
Abstract
backgroundEach year, millions of Americans receive evidence-based psychotherapies (EBPs) like cognitive behavioral therapy (CBT) for the treatment of mental and behavioral health problems. Yet, at present, there is no scalable method for evaluating the quality of psychotherapy services, leaving EBP quality and effectiveness largely unmeasured and unknown. Project AFFECT will develop and evaluate an AI-based software system to automatically estimate CBT fidelity from a recording of a CBT session. Project AFFECT is an NIMH-funded research partnership between the Penn Collaborative for CBT and Implementation Science and Lyssn.io, Inc. ("Lyssn") a start-up developing AI-based technologies that are objective, scalable, and cost efficient, to support training, supervision, and quality assurance of EBPs. Lyssn provides HIPAA-compliant, cloud-based software for secure recording, sharing, and reviewing of therapy sessions, which includes AI-generated metrics for CBT. The proposed tool will build from and be integrated into this core platform.
methodsPhase I will work from an existing software prototype to develop a LyssnCBT user interface geared to the needs of community mental health (CMH) agencies. Core activities include a user-centered design focus group and interviews with community mental health therapists, supervisors, and administrators to inform the design and development of LyssnCBT. LyssnCBT will be evaluated for usability and implementation readiness in a final stage of Phase I. Phase II will conduct a stepped-wedge, hybrid implementation-effectiveness randomized trial (N = 1,875 clients) to evaluate the effectiveness of LyssnCBT to improve therapist CBT skills and client outcomes and reduce client drop-out. Analyses will also examine the hypothesized mechanism of action underlying LyssnCBT. DISCUSSION: Successful execution will provide automated, scalable CBT fidelity feedback for the first time ever, supporting high-quality training, supervision, and quality assurance, and providing a core technology foundation that could support the quality delivery of a range of EBPs in the future.
trial registrationClinicalTrials.gov; NCT05340738 ; approved 4/21/2022.
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Identifiers
What OpenQuestion holds
Registered trials
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.