ArticleDIS. Designing Interactive Systems (Conference)2018
"It's hard to argue with a computer:" Investigating Psychotherapists' Attitudes towards Automated Evaluation.
Article in DIS. Designing Interactive Systems (Conference), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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.
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.
Who cites it
16 citing papers in PubMed.
- Mental health practitioners' perceptions and adoption intentions of AI-enabled technologies: an international mixed-methods study.BMC health services research · 2025Article
- Harnessing Innovative Technologies to Train Nurses in Suicide Safety Planning With Hospital Patients: Formative Acceptability Evaluation of an eLearning Continuing Education Training.JMIR formative research · 2024Article
- Quality of interaction between clinicians and artificial intelligence systems. A systematic review.Future healthcare journal · 2024Review
- Machine-Learning-Based Prediction of Client Distress From Session Recordings.Clinical psychological science : a journal of the Association for Psychological Science · 2024Article
- Improving the quality of counseling and clinical supervision in opioid treatment programs: how can technology help?Addiction science & clinical practice · 2024Article
- Seeing Your Stories: Visualization for Narrative Medicine.Health data science · 2024Article
- Attitudes Toward the Adoption of 2 Artificial Intelligence-Enabled Mental Health Tools Among Prospective Psychotherapists: Cross-sectional Study.JMIR human factors · 2023Article
- Systematic review of machine learning utilization within outpatient psychodynamic psychotherapy research.Frontiers in psychiatry · 2023Review
- Knowledge and Attitudes Toward an Artificial Intelligence-Based Fidelity Measurement in Community Cognitive Behavioral Therapy Supervision.Administration and policy in mental health · 2022Article
- Automated evaluation of psychotherapy skills using speech and language technologies.Behavior research methods · 2022Article
- Harnessing Innovative Technologies to Train Nurses in Suicide Safety Planning With Hospitalized Patients: Protocol for Formative and Pilot Feasibility Research.JMIR research protocols · 2021Article
- Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping Review.JMIR mental health · 2021Article
- Care Managers and Role Ambiguity: The Challenges of Supporting the Mental Health Needs of Patients with Chronic Conditions.Computer supported cooperative work : CSCW : an international journal · 2021Article
- Development and Evaluation of ClientBot: Patient-Like Conversational Agent to Train Basic Counseling Skills.Journal of medical Internet research · 2019Article
- Design feasibility of an automated, machine-learning based feedback system for motivational interviewing.Psychotherapy (Chicago, Ill.) · 2019Article
- Evaluation of a Level I trauma center provider training in patient-centered alcohol brief interventions using the Behavior Change Counseling Index rated by standardized patients.Trauma surgery & acute care open · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
We present CORE-MI, an automated evaluation and assessment system that provides feedback to mental health counselors on the quality of their care. CORE-MI is the first system of its kind for psychotherapy, and an early example of applied machine-learning in a human service context. In this paper, we describe the CORE-MI system and report on a qualitative evaluation with 21 counselors and trainees. We discuss the applicability of CORE-MI to clinical practice and explore user perceptions of surveillance, workplace misuse, and notions of objectivity, and system reliability that may apply to automated evaluation systems generally.
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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.