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Talking to a Human as an Attitudinal Barrier: A Mixed Methods Evaluation of Stigma, Access, and the Appeal of AI Mental Health Support
ABSTRACT
Background:
Many individuals who could benefit from evidence-based psychotherapy do not receive it due to evaluation-sensitive concerns (e.g., shame, stigma, fear of judgment, privacy) and structural barriers (e.g., limited provider availability, long waitlists, scheduling challenges). Conversational artificial intelligence (AI) tools are increasingly used for mental health support, yet it remains unclear which therapy barriers AI helps mitigate.
Objective:
To examine whether evaluation-sensitive (shame/stigma) and structural (access, cost/coverage) barriers to psychotherapy predict perceived helpfulness of a purpose-built AI mental health conversational tool (Ash), and whether effects differ by prior therapy experience or user engagement.
Methods:
In a cross-sectional survey of Ash users (N=395), participants rated Ash’s helpfulness (1-5) and described barriers to therapy. Open-text responses were coded for shame/stigma, access, and cost/coverage themes. Linear regressions examined associations between barriers and perceived helpfulness, adjusting for demographics and mental health covariates, with moderation by prior therapy experience. Negative binomial regressions tested whether barriers predicted platform usage.
Results:
In the primary model (n=374), shame/stigma (B=.45, p<.001) and access barriers (B=.31, p=.020) predicted higher perceived helpfulness, whereas cost/coverage did not (B=.13, p=.262). Prior therapy experience moderated the shame effect (interaction B=.56, p=.036): shame predicted higher helpfulness among therapy-experienced users (Δ=.62, p<.001) but not therapy-naïve users (Δ=.03, p=.877). Among therapy-experienced participants comparing Ash with past therapy (n=258), shame/stigma (B=.75, p<.001) and access barriers (B=.51, p=.006) predicted rating Ash more favorably. Access barriers predicted higher engagement (IRR=1.64, p<.001), and cost/coverage barriers predicted 70% more sessions (IRR=1.70, p<.001), whereas shame/stigma was not associated with total sessions (IRR=.80, p=.094).
Conclusions:
AI mental health support was perceived as most helpful among users facing shame/stigma and access barriers to psychotherapy, suggesting it may function as an evaluation-safe adjunct or alternative, particularly for therapy-experienced individuals. Access and cost barriers were most predictive of usage intensity, highlighting unmet need in this population. These findings highlight the importance of aligning the design, implementation, and safety guardrails of conversational AI tools for emotional support with user-reported barriers.
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Copyright
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