2024 Civil Registration and Vital Statistics Applied Research Training Initiative

Key dates

Monday, 29 January 2024 Applications open
Monday, 4 March 2024 Deadline for submission of applications
Friday, 15 March 2024 Notification of successful applicants
April - June 2024 Online training modules 1, 2 and 3
August 2024 First face-to-face training workshop
September - November 2024 Online training modules 4, 5 and 6
January 2025 Second face-to-face training workshop
January - February 2025 Submission of final research paper and dissemination of findings
March 2025 CRVS Research Forum


The CRVS applied research training (CART) initiative focuses on enhancing CRVS systems through supporting applied research on strategies, interventions, and tools. This involves designing projects to address practical questions, employing robust methodologies, and identifying key personnel for effective implementation and publication. The need to strengthen practitioners' research capacity is evident, as highlighted in the Asia-Pacific CRVS research forum held in 2023. Challenges include limited time for essential research activities within program settings, hindering the completion and utilization of quality research. Additionally, building a critical mass of trained researchers is crucial, as talented individuals may not engage in research, leading to a shortage of research capacity.

This project aims to strengthen the capacity of CRVS practitioners to conduct high quality relevant research. It builds on a successful model implemented previously by the International Union Against Tuberculosis and Lung Disease and Medicins Sans Frontieres to build operational research capacity among health professionals, known as the “SORT-IT” model. While the project will be adapted for the CRVS context, the approach will be similar and incorporate the essential element as identified by Kumar et al. 2020, Zachariah et al. 2020 and Zachariah et al. 2016. Additionally, lessons reflecting the significant advances in elearning in recent years be considered to find the best blend and types of learning to achieve the objectives.

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