In partnership with UNDP Sierra Leone, this project assessed the prevalence, incidence, trends, and patterns of sexual and gender-based violence (SGBV) in Sierra Leone through a mixed-methods approach and innovate machine learning techniques. By analyzing the structural and root causes that contribute to SGBV, the final report provided targeted policy recommendations to prevent and respond to this phenomenon through a unified national response.

With regards to quantitative methods, DPA mapped, accessed, and analyzed national and international data sources to uncover which drivers might have a higher statistical influence on the prevalence of SGBV. With this information, the research team created advanced machine learning techniques to identify the most relevant indicators and their relationship to SGBV. This analysis was complemented with qualitative data collected through a systematic literature review (over 50 pieces, supported by the ecological model), semi-structured interviews with survivors of SGBV, and focus group discussions with key CSO and government stakeholders.