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

Globally, road traffic collisions are the leading cause of death among adolescents. India has an increasing burden of road injuries among adolescents. Our previous qualitative work found that understanding context is critical to enable implementation of effective interventions to reduce the burden of road injury among adolescents. On 29th July 2020, the New Education Policy was introduced by the Government of India which proposes accessible education for special groups by providing bicycles. Knowing the vulnerability of pedestrians and cyclists, we need measures to ensure equitable and safe routes to school, to achieve the goal of accessible education for every child. We will use a sequential, mixed methods approach to evaluate the road environment quantitatively using the International Road Assessment Programme (iRAP) Star Rating for School App, followed by qualitative investigation with key stakeholders to assess system capacity for implementation of safe school routes in Madhya Pradesh. The proposed met