Panel Discussion 34 - Surviving Sociopolitical Challenges: Leveraging Clinical and Analytic Strategies to Address Suicide Disparities in Sexual and Gender Minoritized Communities

Sexual and gender minoritized (SGM) communities experience significant disparities in self-injurious thoughts and behaviors and continue to see increasing rates of suicide deaths despite population-wide declines. SGM persons are impacted by intersectional and multi-level risks and vulnerabilities, including structural, interpersonal, and internalized factors. Consequently, conventional analytic methods may not be adequate to understand how disparities emerge and are maintained, and evidence-based interventions may require adaptations in content and/or delivery to increase their reach and effectiveness. The current sociopolitical context of heightened legislative discrimination against SGM communities increases both the need for evidence-based care, and barriers to care access. Thus, conducting work in the service of SGM communities will require novel solutions. This panel presents a timely opportunity for the ABCT community to engage with these discussions in the face of mounting challenges.

Aligning with the 2025 convention theme to bridge rigorous science with affirming therapies, this panel brings together six experts who will discuss how to leverage innovative and rigorous clinical and analytic strategies to understand and intervene on suicide disparities. This panel represents diverse stakeholder perspectives (trainees, researchers, clinicians, community partners), across innovation and practice settings (universities, academic medical centers, emergency departments, community health and nonprofit organizations, private practice). Discussions will focus on: (1) describing the magnitude of and nuances in disparities; (2) sharing expertise on advanced quantitative and methodological approaches to examine complex, co-occurring risks and vulnerabilities, and identify modifiable targets for interventions (e.g., ecological momentary assessment and computed affective dynamics, machine learning and data driven models for intersectional analyses); (3) contributing insights on tailoring intervention content (e.g., to address oppression-based and minority stress) and delivery (multilevel and integrated interventions, community-participatory strategies) to meet the needs of SGM communities.