Background Women who have survived violence experience negative consequences on their sexual and reproductive health, unsafe abortions, sexually transmitted infections, traumatic fistulas, and are twice as likely to be diagnosed with cervical cancer. They also experience anxiety, depression, and alcohol use disorders. Rates of violence against women and gender based discrimination continue to remain high despite legislative action by governments. With COVID-19 pandemic, the situation has become alarming demanding ever more serious action in protecting the rights of women and addressing Gender Based Violence (GBV) and discrimination across the globe. The United Nations (UN) Convention on the Elimination of all Forms of Discrimination Against Women (CEDAW) has been considered as an important instrument towards encouraging governments to take action towards protecting the rights of women and addressing gender based discrimination through legal reforms. It has been 40 years since the United Nations (UN) Con

Background Achieving the UN Sustainable Development Goal 3.4, requires complex evidence-based interventions to be implemented sustainably in local settings. However, effective interventions are often not fully implemented, and some not at all, due to factors that could have been identified during process evaluations. Process evaluations can help to design interventions, optimise implementation, and inform sustainability and scale but are also time and resource intensive. Thus, most process evaluations are commonly performed at or near study end, and findings are not rapidly fed back to end-users. Computational linguistics could be a potential solution to enhance the process evaluation in a timely and valid manner. Computational linguistics uses advanced text mining and machine learning to discover linguistic patterns in natural language and link these patterns to process evaluation domains such as implementation measures of fidelity, acceptability, and appropriateness. Aim This study aims to use e