Sexual and Gender Diverse Mental Health: The Role of Family Relationships and School Experiences
Mental health disparities among sexual and gender diverse (SGD) youth are well documented, with elevated rates of depression, anxiety, and suicidality consistently linked to minority stress processes. However, less work has systematically examined how proximal relational systems—particularly family and school contexts—interact to shape heterogeneous risk and resilience profiles within SGD populations. Moving beyond individual-level models, a systems-oriented approach is needed to identify which combinations of relational and environmental factors most strongly explain differences in well-being among youth with various patterns of marginalized social positions.
An MRI-funded project led by Dr. Ryan Watson (University of Connecticut) addresses this need in Sexual and Gender Diverse Mental Health: The Role of Family Relationships and School Experiences. The study advances an interactional framework, examining how family dynamics and school climates jointly contribute to mental health outcomes among sexual and gender minority youth. Rather than asking whether family or school contexts matter independently, the project investigates how these systems operate together to amplify risk or promote resilience.
To accomplish this, the research team (Dr. Watson and Peter McCauley, a graduate student at the University of Connecticut) is harmonizing two of the largest national datasets focused on LGBTQ+ adolescents: the 2022 UConn/Human Rights Campaign LGBTQ+ National Teen Survey (N = ~17,000) and The Trevor Project’s 2022 National LGBTQ Youth Mental Health Survey (N = ~34,000). By aligning comparable measures across both datasets—including indicators of family acceptance and rejection, teacher support, school safety and climate, and mental health outcomes such as stress, anxiety, and depression—the team substantially increases statistical power and analytic precision. This harmonization enables the detection of nuanced subgroup patterns that would be difficult to observe within a single dataset.
Analytically, the project employs Exhaustive Chi-square Automatic Interaction Detection (ECHAID), a decision-tree method designed to identify meaningful interaction effects and subgroup constellations. Unlike traditional regression approaches that test predefined interactions, ECHAID iteratively partitions the sample to reveal combinations of predictors that most strongly differentiate mental health outcomes. This approach is particularly well suited for identifying real-world intersections—for example, how low family support combined with specific school climate conditions and identity-related factors may delineate especially high-risk profiles, or conversely, how strong teacher support may buffer youth in otherwise strained family contexts.
Importantly, the study incorporates qualitative interpretation through focus groups with youth and mental health stakeholders. This step enhances ecological validity by grounding statistical subgroup findings in lived experience and ensuring that analytic categories reflect realities meaningful to young people and practitioners. The integration of large-scale quantitative modeling with stakeholder-informed interpretation strengthens the project’s translational capacity.
The ultimate goal is to generate actionable, targeted guidance for families, educators, clinicians, and policymakers. By situating sexual and gender diverse mental health within interacting relational systems, this project advances the field beyond deficit-based individual models. It provides a data-driven roadmap for identifying where intervention is most urgently needed—and where protective relational processes can be cultivated to support thriving among SGD youth populations.