From Contraception to Menopause: Identifying and Addressing Reproductive Health Misinformation

Description: Reliable information about reproductive health has never been more important. However, taboos, legal restrictions, and a dearth of women's health research have enabled misinformation to flourish, with dire implications for health outcomes. This talk will provide an overview of the prevalence, characteristics, and impacts of reproductive health misinformation. It will reveal why myths about reproductive health are so compelling and what dynamics allow them to propagate. Reproductive health misinformation will be contextualized within health misinformation more broadly. Given the popularity of the internet as a source of information on reproductive health, this talk will provide background on the landscape of online reproductive health information and the role of content moderation policies. This talk will train attendees to identify and counter reproductive health misinformation in their own work, from public health to medical libraries.

Guest Speaker: Jennifer Neda John recently graduated with honors from Stanford University, where she majored in Human Biology with a concentration in Women’s Health and minored in Computer Science. She completed an honors thesis on misinformation about abortion and studied Covid-19 anti-vaccine misinformation as a research assistant at the Stanford Internet Observatory. She was previously a research assistant at the National Institute of Child Health and Human Development, where she studied misinformation about polycystic ovarian syndrome and conducted qualitative and quantitative research on fertility preservation. She is interested in health misinformation, communication, and literacy, women’s reproductive health, and education.
September 27, 2023
Time: 2:00 PM - 3:00 PM CDT



Register Here <https://urldefense.com/v3/__https://www.nnlm.gov/training/class/contraception-menopause-identifying-and-addressing-reproductive-health__;!!GNU8KkXDZlD12Q!_FjDWmoj5_QCVDggGH2VEgcyxuxs9gau_bLeokxL4Fg4oHwPi9IjyfyUnbDhfd3VP4vjSeeIKp3WzvT51BONW-0xQdjvvgdm$ [nnlm[.]gov]>



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Best wishes,

Dana