Evidence map›Paper›PMID 34537885›Full record

ArticleAdministration and policy in mental health2022

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

Torrey A Creed, Patty B Kuo, Rebecca Oziel, Danielle Reich, Margaret Thomas, Sydne O'Connor, Zac E Imel, Tad Hirsch, Shrikanth Narayanan, David C Atkins

Open access · greenAbstract read
In one paragraph

Article in Administration and policy in mental health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
2.4field-weighted citation impact, top 12% of its field
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

17 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Article
  4. Review
  5. Article
  6. Characterizing Fidelity Monitoring Practices in Community Behavioral Health Care.The journal of behavioral health services & research · 2026
    Article
  7. Article
  8. Article
  9. Review
  10. Review
  11. Machine-Learning-Based Prediction of Client Distress From Session Recordings.Clinical psychological science : a journal of the Association for Psychological Science · 2024
    Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Article
  17. 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

10 authors at 6 institutions in 1 country.

Torrey A CreedPerelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. tcreed@pennmedicine.upenn.edu.ORCID 0000-0003-0860-2498
Patty B KuoDepartment of Educational Psychology, University of Utah, Salt Lake City, UT, USA.
Rebecca OzielPerelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Danielle ReichPerelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Margaret ThomasNortheastern University, Boston, MA, USA.
Sydne O'ConnorPerelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Zac E ImelDepartment of Educational Psychology, University of Utah, Salt Lake City, UT, USA.
Tad HirschNortheastern University, Boston, MA, USA.
Shrikanth NarayananSignal Analysis and Interpretation Lab, University of Southern California, Los Angeles, CA, USA.
David C AtkinsDepartment of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA.
Penn Center for AIDS Research · USNortheastern University · USUniversity of Utah · USUniversity of Pennsylvania · USUniversity of Southern California · USUniversity of Washington · US

Funding

Technology-supported training and quality assurance for psychosocial interventionsR56MH118550 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI ATKINS, DAVID CHARLES, CREED, TORREY A. · 2019 to 2019
$832k
NIMH NIH HHS R56 MH118550
6 · The paper itself

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.

Indexed as

Artificial IntelligenceCognitive Behavioral TherapyAttitudeFocus GroupsHumansResearch DesignArtificial intelligenceCognitive behavioral therapyCommunity mental healthFidelityMachine learningSupervisionTechnology

Identifiers

PMID34537885
PMCPMC8930782
OpenAlexW3163021855

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.