Talking to a Human as an Attitudinal Barrier: A Mixed Methods Evaluation of Stigma, Access, and the Appeal of AI Mental Health Support
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2026
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| author | Stamatis, Caitlin A. Wolfe, Emma C. Malgaroli, Matteo Hull, Thomas D. |
| author_facet | Stamatis, Caitlin A. Wolfe, Emma C. Malgaroli, Matteo Hull, Thomas D. |
| contents | 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 ($Δ$=.62, p<.001) but not therapy-naive users ($Δ$=.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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_09575 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Talking to a Human as an Attitudinal Barrier: A Mixed Methods Evaluation of Stigma, Access, and the Appeal of AI Mental Health Support Stamatis, Caitlin A. Wolfe, Emma C. Malgaroli, Matteo Hull, Thomas D. Human-Computer Interaction Computers and Society 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 ($Δ$=.62, p<.001) but not therapy-naive users ($Δ$=.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. |
| title | Talking to a Human as an Attitudinal Barrier: A Mixed Methods Evaluation of Stigma, Access, and the Appeal of AI Mental Health Support |
| topic | Human-Computer Interaction Computers and Society |
| url | https://arxiv.org/abs/2604.09575 |