In an ever-evolving landscape of global public health, it is imperative to recognize the profound impact of gender on health data, policy, and practice. Our course is designed to raise awareness of the essential need for a gender-sensitive approach in public health data management and analysis. “Gender Foundations in Health Data” will equip learners with practical skills and insights into integrating a gender perspective into data collection, analysis, and utilization.
“Gender Foundations in Health Data” equips learners with practical skills and insights to seamlessly integrate a gender perspective into data collection, analysis, and utilization. The course instructors, Michelle R. Kaufman, PhD, and Tahilin Sanchez Karver, PhD, MPH, are excited to guide you through this new learning opportunity.
We understand the importance of accessibility, and to that end, all course videos have been translated into multiple languages through closed captioning.
For those seeking a certificate of completion, Coursera offers this option for a fee of US$49. However, we are committed to making this knowledge accessible to all, especially in low- and middle-income countries (LMICs). Therefore, we encourage learners from LMICs to apply for a discount of up to 75% on the certificate cost directly through Coursera.
Data for Health partners made important contributions to the course, so a tremendous thank you to Vital Strategies, CDC Foundation, World Health Organization, Global Health Advocacy Incubator, United Nations Economic and Social Commission for Asia and the Pacific, and UN Economic Commission for Africa.
Those D4H partners who want to receive a free course certificate of completion from the Johns Hopkins Bloomberg School of Public Health can send an email to Marcela Banegas, Research Assistant at the Gender Equity Unit, at firstname.lastname@example.org for access.
Enroll today, and together we can make a significant impact on public health data practices worldwide. To learn more and to get started, please visit our course page: Gender Foundations in Health Data.