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Towards semi-automatic monitoring of delivery behavioral interventions Open Access (recommended)

Descriptions

Resource type(s)
Poster
Keyword
fidelity monitoring
behavioral interventions
automatic fidelity
Rights
Attribution-NonCommercial 3.0 United States

Creator
Gallo, Carlos
Berkel, Cady
Sandler, Irwin N.
Brown, C Hendricks
Abstract
Traditional methods for monitoring implementation of evidence-based programs require labor-intensive quality assessments. These assessments generally involve human observation, hence becoming a major bottleneck in the monitoring of implementation. We present the development of computer-based methods for implementation quality measurement. Firstly, we describe 7 principles of automatization necessary tosystematically identify critical dimensions for analysis.Secondly, we present an automatic classifier that uses linguistic patterns to classify sessions between high-fidelity versus low-quality. The classifier was trained on an initial corpus of 22 transcripts which were coded for high and low quality by humans and replicated by machine learning. Our goal is to develop efficient methods that reduce the burden of quality monitoring, and provide timely feedback for delivery supervisors.Overall, we want to explore to what extent quality monitoring can be done with automatic methods [1,3,4].
Publisher
DigitalHub. Galter Health Sciences Library
Language
English
Subject: MESH
Machine Learning
Evidence-Based Practice
Quality Control
Behavior Therapy
Subject: LCSH
Computational linguistics
DOI
10.18131/G34S5W
ARK
ark:/c8131/g34s5w

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