ABSTRACT
Background: There is a critical need to address mental health needs across the globe, especially in low and middle-income countries where mental health disparities are pervasive, including among children. The global mental health disparities suggest an imperative for culturally and contextually-congruent mental health services models that expand upon the existing services and interventions for these groups. Rigorous research is a key tool in providing the scientific evidence to inform public policy and practice efforts to effectively address these needs. Yet, there is a limited number of researchers, especially those from diverse backgrounds, who study these issues. In this paper, we describe the "TrainingLEADers to Accelerate Global Mental Health Disparities Research" (LEAD) program, a research training program funded by the National Institute on Minority Health and Health Disparities and focused on global mental health disparities research for early career researchers from under-represented minority groups. Methods: The LEAD program is designed as a two-phase training program for advanced pre-doctoral students, postdoctoral fellows, and junior faculty from diverse backgrounds in the U.S., including groups underrepresented in biomedical, behavioral, clinical and social sciences research, interested in global mental health disparities research. Trainees are matched with mentors and participate in an intensive 12-week program. Discussion: The LEAD program seeks to provide a robust platform for the development, implementation and expansion of evidence-based culturally and contextually-congruent interventions and services models addressing global mental health disparities across the life cycle, especially in low-resource communities in the global context. By producing a sustainable network of well-trained investigators from underrepresented backgrounds, LEAD will potentially contribute to the shared lessons and efforts relevant to addressing global mental health disparities and improving care for vulnerable populations in low-resource settings.