A step toward the future? evaluating GenAI QPR simulation training for mental health gatekeepers

Abstract

Background Suicide remains a leading cause of preventable death, placing a significant burden on healthcare systems worldwide. Effective suicide prevention relies not only on mental health professionals but also on well-trained gatekeepers, including primary care providers, emergency physicians, and community healthcare workers. Traditional training programs, such as Question, Persuade, and Refer (QPR), require structured practice and continuous reinforcement to ensure competency. The integration of artificial intelligence (AI)-based simulators into medical training offers a promising, scalable approach for improving suicide prevention skills in healthcare settings. This study evaluates the effectiveness of an AI-driven simulator in enhancing QPR-related competencies. Methods A total of 89 adult participants from the community, all of whom were mental health professionals (including social workers, occupational therapists, speech therapists, and physicians), completed pre- and post-intervention assessments measuring self-efficacy and willingness to support individuals at risk of suicide. Participants engaged in real-time interactions with an AI-powered simulator that mimicked conversations with at-risk individuals, enabling dynamic practice of QPR (Question, Persuade, Refer) skills. Data were collected in June 2024. Quantitative data were analyzed using paired t -tests and Pearson correlations, while qualitative feedback was examined through content analysis. Results Post-intervention self-efficacy scores showed a significant increase, with a large effect size (Cohen’s D = 1.67). Willingness-to-support scores demonstrated a slight but non-significant improvement. Higher QPR self-efficacy correlated positively with increased willingness to support. Qualitative feedback indicated that participants found the simulator realistic and beneficial for skill acquisition, although some expressed concerns regarding the potential reduction of human interaction in mental health training. Conclusion AI-driven simulators hold promise as scalable, accessible, and clinically relevant tools for suicide prevention training. Their integration into medical education and clinical settings could improve the preparedness of healthcare providers, primary care physicians, and frontline medical staff in identifying and managing suicide risk. These findings support the adoption of digital health innovations to enhance medical training and public health interventions.

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