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TRUVACE RECORD VERSION
record: TRV-2026-1597
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-16T18:13:23.363672Z
status: published
lens: p_space
sector: health
headline: AI-Mediated Mental Health Support: The Role of Attachment Orientation and Psychological Distress
dek: General-purpose generative AI (GenAI) models are increasingly serving as quasi-social agents, representing a novel form of informal AI-mediated help-seeking. This study examined associations between adult attachment orientations (Experiences in Close Relationships-Relationship Structures questionnaire), psychological distress (Depression Anxiety Stress Scales-21), and the use of GenAI for mental health support. In a nonprobability sample of 584 Israeli adult GenAI users, 29.3 percent reported having turned to th…
gain_title: (none)
problem_title: Nearly half of severely distressed respondents had used consumer generative AI for mental health help, which raises concerns about crisis-response quality and displacement of clinical care that have not yet been evaluated.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Nearly half of severely distressed respondents had used consumer generative AI for mental health help, which raises concerns about crisis-response quality and displacement of clinical care that have not yet been evaluated.
problem_evidence: The high uptake among severely distressed users (47.0 percent) points to potential safety concerns | clinical-displacement risks of consumer-facing AI use
quick_read: A survey study of 584 Israeli adults who use generative AI examined whether attachment style and psychological distress were linked to turning to chatbots for emotional support, coping advice, or mental health help. About 29.3 percent had done so at least once, and the share rose to 47.0 percent among those reporting severe distress.

The results suggest that people in greater distress are the ones most drawn to these general-purpose tools, which the authors describe as accessible and low-barrier. Because the data are self-reported and cross-sectional, they do not show whether the tools help or harm, and the authors call for systematic safety evaluation.
limitation: The sample is nonprobability and limited to Israeli adults, and the usage figures are self-reported and include any contact from a single exchange to sustained use, so they do not show the depth of reliance or outcomes.
tag: Evidence-backed problem
key_points: Distress was the strongest psychological correlate of using generative AI for mental health support. | Younger age and female gender also predicted use. | Attachment anxiety was associated with use, but the link was no longer significant once distress was included. | Attachment avoidance was negatively but nonsignificantly associated with use.
rundown: The study used a nonprobability sample of 584 Israeli adult generative AI users. Attachment orientations were measured with the Experiences in Close Relationships-Relationship Structures questionnaire, and distress with the Depression Anxiety Stress Scales-21.

Hierarchical logistic regression was used to test the predictors. The reported usage figures cover any contact, from a single exchange to sustained use, so they do not distinguish casual from heavy reliance.

The authors frame general-purpose generative AI as a quasi-social agent and a new form of informal AI-mediated help-seeking. They urge evaluation of safety, crisis-response quality, and the risk that these tools displace clinical care.
sources:
- peer_reviewed | Cyberpsychology, Behavior, and Social Networking | https://doi.org/10.1177/21522715261494423 | 2026-09-29
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