The following is a guest post by Jessica Fehringer, PhD, MEASURE Evaluation
I’ve worked in research and with researchers for a while, and often I hear people say that it doesn’t make sense to address gender in their research. When we think about science, we think about proving or disproving a hypothesis. In other words, A leads to B — like it or not.
But, we all know that a multitude of structural and social factors — including gender — can influence the processes we follow, from healthcare decision-making or dating outside of work, to data collection, analysis, and reporting in our work. Not only is it important to think of gender in relation to the science you’re doing, it is a disservice to your science not to.
I work in the realm of global health. Maybe some researchers have thought that integrating gender into their research is a matter of “add women and stir.” That’s absolutely incorrect — and not just because gender norms affect both women and men. Gender integration must consider the gender constraints that shape health-seeking behavior and health outcomes. But even reaching into other aspects of research design, gender integration must also consider how gender norms may impact data collection, data analysis, and reporting.
For example, just because a family planning activity focuses on data collection with women does not mean it is gender-integrated. A researcher also should consider in activity logistics how gender norms may affect the timing of participant availability, the data collection location one might choose, or who should be interviewing whom. A researcher should consider local gender norms around family planning and address those in the interview guide.
Females are not the only ones negatively affected by gender inequality. For example, norms around what a “real man” is can lead to poor health consequences for men, such as sexual behavior that increases risk of acquiring or transmitting HIV. Male norms sometimes deter men from seeking healthcare and may mean they are unavailable at different times and in different places, based on what the local norms are for male activities.
At my project — MEASURE Evaluation, funded by the United States Agency for International Development (USAID) — health information systems are our focus, especially improving data collection and quality for use in decision making. We’ve discovered gender affects these information systems in many ways. More health data may be available on women because they are the prime users of health services. However, less data may be available in civil registration and vital statistics, because women are often disadvantaged in the ability to register themselves and their children—which affects the population denominators used for disease surveillance and cause of mortality data, for example. Health information systems can explicitly measure certain gender equity concerns, such as health decision-making, couples communication, and gender-based violence. Therefore, these elements should be built into systems. MEASURE Evaluation has published a standard operating procedure for integrating gender in monitoring, evaluation, and research. The SOP is designed to help program planners and decision makers consider how gender impacts data and programs on family planning, HIV/AIDS, orphans and vulnerable children, emerging infectious diseases, tuberculosis, and malaria. For a quick look at the considerations on integrating gender, see also a short video in English and French.
For more information about MEASURE Evaluation, visit www.measureevaluation.org.