Claims about what might improve or harm our health are everywhere. Some of these claims are reliable, but many are not. People often don’t know how to tell the difference. Making decisions based on unreliable claims wastes resources and can result in unnecessary suffering. This problem was exacerbated by the COVID-19 pandemic, which was accompanied by an “infodemic”— an overload of information, including false or misleading information.
Continuing our 'state of the evidence' series exploring insights from the DEP – this blog explores and shares key insights from the evidence on the Middle East and North Africa region.
Côte d'Ivoire faces substantial nutritional challenges, with 17 percent of children under five experiencing stunting and over six percent exhibiting wasting in 2021. Overall, 18 percent of the population grapples with the imminent threat of acute food insecurity. In addition, many communities lack access to safe drinking water and sanitation, which can transmit diseases such as cholera, dysentery and polio. Recurrent infections can also be a major cause of malnutrition and child stunting. 3ie’s WACIE Helpdesk supported the government’s response to the crisis by providing up-to-date evidence to inform and improve its strategy.
At 3ie, we recently tested a machine learning technique on a previously completed randomized controlled trial (RCT) of a school-based gender attitude change program in Haryana, India to answer this question. We share a few key findings and benefits of employing this method.
3ie, en partenariat avec le gouvernement du Bénin, lance l’évaluation d'impact d'une initiative révolutionnaire : un programme de nutrition pratique et évolutif pour améliorer les résultats en matière de santé maternelle et infantile.
3ie, in partnership with the Government of Benin, is launching an impact evaluation of a groundbreaking initiative: a practical, scalable nutrition program to improve maternal and infant health outcomes.
