Mental Health Counseling From Conversational Content With Transformer-Based Machine Learning.

ABSTRACT:

Importance

Use of asynchronous text-based counseling is rapidly growing as an easy-to-access approach to behavioral health care. Similar to in-person treatment, it is challenging to reliably assess as measures of process and content do not scale.

Objective

To use machine learning to evaluate clinical content and client-reported outcomes in a large sample of text-based counseling episodes of care.

Design, setting, and participants

In this quality improvement study, participants received text-based counseling between 2014 and 2019; data analysis was conducted from September 22, 2022, to November 28, 2023. The deidentified content of messages was retained as a part of ongoing quality assurance. Treatment was asynchronous text-based counseling via an online and mobile the

SUBMITTER: Imel ZE 

PROVIDER: S-EPMC10804269 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature