Introduction: The M(H)IND [Mental Health INtegrated Development] initiative aims to contribute to the achievement of Sustainable Development Goal No. 3 with regard to its mental health component (Overall Objective). The project, lasting three years, is carried out by Amref Health Africa in partnership with Caritas, BBC Media Action, and the University of Verona. It aims to fight stigma, establish dedicated community-based and clinical services, and build the capacity of social and health personnel to offer mental health care. The research component aims to evaluate the real-world effectiveness and scalability of the WHO’s psychological intervention named “Self Help Plus” (SH+) in South Sudan, where it will be implemented for the promotion of mental health and psychological wellbeing in the general population. Secondary aims include the evaluation of the implementability of SH+ and its degree of fidelity, as well as assessing factors associated with its implementation and effectiveness. Methods: A prospective hybrid type-1 non-randomized follow-up study design will be used in a large-scale, nationwide intervention (expected N > 5000). Each participant will undergo a baseline assessment before taking part in the SH+. A follow-up evaluation will take place immediately after the completion of the intervention. Measures include: the Kessler Psychological Distress Scale (K6); the World Health Organization-Five Well-Being Index (WHO-5); the Patient Health Questionnaire-9 (PHQ-9); measures of appropriateness, feasibility, and acceptability. Results: Qualitative and quantitative results from the formative research will be presented. A summary of the general and country-specific barriers to implementation will be presented following the researchers’ in-loco field mission in the areas of intervention. Conclusion: Scalable mental health intervention models are needed for emergency settings, low-income countries, and countries with no or very limited mental health care systems. Common and country-specific barriers can hinder the implementation of effective, scalable models.