Mental Distress, Label Avoidance, and Use of a Mental Health Chatbot:
Results from a U
Abstract
Objectives
To pilot test a mental health chatbot designed to screen users for
psychological distress and refer to resources, addressing the strain on
the U.S. behavioral healthcare workforce and exacerbated behavioral
health challenges among U.S. adults due to COVID-19.
Methods
Data were collected via a national, cross-sectional, internet-based
survey of U.S. adults. Measures included demographics, symptoms, stigma,
technology acceptance, willingness to use the chatbot, and chatbot
acceptability. Relationships between these variables were explored using
chi-square tests, correlations, and logistic regression.
Results
Of 222 participants, 75.7% completed mental health screening within the
chatbot. Participants found the chatbot to be acceptable. Demographic
predictors of chatbot use included being White or Black/African
American, identifying as Hispanic/Latino, having dependents, having
insurance coverage, having used mental health services in the past,
having a diagnosed mental health condition, and reporting current
distress. Logistic regression produced a significant model with
perceived usefulness and symptoms as significant positive predictors of
chatbot use for the overall sample, and label avoidance as the only
significant predictor of chatbot use for those currently experiencing
distress.
Discussion
Chatbot technology may be a feasible and acceptable way to screen large
numbers of people for psychological distress and disseminate mental
health resources. Since label avoidance was identified as the single
significant predictor of chatbot use among currently distressed
individuals, chatbot technology may be one way to circumnavigate stigma
as a barrier to engagement in behavioral health care.
Limitations
Recruitment through Amazon’s mTurk limits generalizability of our
findings, and chi-square test effect sizes were small.