Web-based personalized normative feedback to decrease stigma and increase intentions to seek help.
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AI chatbot-based interventions offer 24/7 support, enhanced self-management, reduced stigma, and appeal to digital-native users.
“These AI chatbot-based interventions offer 24/7 support, enhanced self-management [10], reduced stigma, and appeal to digital-native users [11].”
From Generative AI Mental Health Chatbots as Therapeutic Tools: Systematic Review and Meta-Analysis of Their Role in Reducing Mental Health Issues (2025) · cites this paper as [10]
Cited by
Sorted by citations
Zhang, Q., Zhang, R., Xiong, Y., Sui, Y., Tong, C., & Lin, F.-H. (2025) · Journal of Medical Internet Research · doi:10.2196/78238 · Cited by 56 · see citing works
“A number of reviews showed promising effects of AI chatbot interventions on reducing mental health distress and improving quality of life [10,12-16] (see Multimedia Appendix 2).”
- Authors
- Oh, H., Pickering, T. A., Schiffman, J., LaBrie, J. W., Soffer-Dudek, N., & Pedersen, E. R.
- ORCID
- Venue
- Stigma and Health
- Published
- 2024-05-02
- ISSN
- 2376-6964 (ISSN-L)
- 2376-6972
- OpenAlex
- W4396580575
- Cited by
- 7
- References
- 0
- Citations by year
- 2026: 3 · 2025: 1 · 2024: 3
- FWCI
- 3.32
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Stigma and Health (publishedVersion, other-oa)
- Funders
- National Institutes of Health (UL1TR001855, UL1TR000130)
- Topics
- Social Sciences › Psychology › Social Psychology › Mental Health Treatment and Access
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Clinical Psychology › Suicide and Self-Harm Studies
- Keywords
- personalized normative feedback, stigma reduction, help-seeking intentions, online intervention, mental health stigma
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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