Talking to a Human as an Attitudinal Barrier: A Mixed Methods Evaluation of Stigma, Access, and the Appeal of AI Mental Health Support

TL;DR

This study investigates how stigma, access, and cost barriers influence perceptions and usage of AI mental health support, revealing that stigma and access issues increase perceived helpfulness especially among therapy-experienced users.

Contribution

It provides new insights into how specific barriers affect perceived helpfulness and engagement with AI mental health tools, highlighting the importance of addressing user-reported obstacles.

Findings

Stigma and access barriers predict higher perceived helpfulness of AI support.

Access barriers are associated with increased engagement and session count.

Cost/coverage barriers do not significantly affect perceived helpfulness.

Abstract

Background: Many people who could benefit from therapy do not receive it. Conversational AI is increasingly used for mental health support, yet it is unclear which barriers AI helps mitigate. We examined whether evaluation-sensitive (shame/stigma) and structural barriers (cost/coverage/access) to psychotherapy predict perceived helpfulness of an AI mental health conversational tool (Ash), and whether effects differ by prior therapy experience or user engagement. Methods: Participants (n=395) 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, with moderation by therapy experience. Results: Shame/stigma (B=.45, p<.001) and access barriers (B=.31, p=.020) predicted higher perceived helpfulness but 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 ($\Delta$=.62, p<.001) but not therapy-naive users ($\Delta$=.03, p=.877). Among therapy-experienced participants (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). Shame/stigma was not associated with total sessions (IRR=.80, p=.094). Conclusions: AI mental health support was perceived as most helpful by users facing shame/stigma and access barriers, particularly for therapy-experienced individuals. Access and cost barriers were most predictive of usage intensity, suggesting unmet needs. Findings highlight the importance of aligning AI tools for emotional support with user-reported barriers.