I’m working more and more with a huge variety of people to set up SNEHA’s new community resource centers (CRC’s) in 20 slum areas of Mumbai. My days are filled with research group meetings looking at data from past projects and the government, concept meetings planning what interventions we will implement through the new CRC, focus group discussions with field staff working at the present CRC’s to share their experiences, and even more meetings with my CRC teammate to synthesize all our findings into one large useable piece.
I also attended the Partnership for Maternal, Newborn and Child Health meeting in Delhi with my mentor Ashoka Fellow Dr. Armida Fernandez where I was able to spend more time with my fellow Young Champion Hellen Kotlolo and meet my present and immediate past ministers of health.
Most importantly I have also found a new way of looking at the work NGOs do. I have always been fascinated by research and the science of proving everything with an equation, with a tendency to reject any methods which have not been rigorously tested and proven. This is why pharmacologic and clinical research have always appeared to me as being more scientific than community-based interventions. Thus one question has often plagued me: “Why do we use the methods we do as NGOs?” And hearing that familiar phrase “evidence-based” touted at the PMNCH meeting in Delhi made me ask myself even more questions: “Why do we carry out home visits to our pregnant women?,” “Why do we use a type of behavioral change communication/work with women’s groups and why do we want to use this particular community resource model?,” and “Have we certified that it is the best method to use of all the available methods?”
My last 3 months at SNEHA has given me the beginnings of an answer to my questions and a new way of thinking about balancing my role as a researcher and social entrepreneur without compromising my scientific conscience. Talking with one of my mentors, Dr. Wasundhara Joshi, has changed my perceptions greatly. Although I can see that many of the methods used for community interventions do not actually have fool-proof evidence, they are the most effective methods for what they’re being used for; I have been reminded again that to create a model for change, the most important thing is being able to connect to the communities we are working with and to adapt our methods for maximal impact as we go along.
Common sense might say we’re working with human beings and the community is not a laboratory, but I’ve often wondered how to balance using strong evidence-based results with simply working with acclaimed and commonly used models. Research evidence often makes your results look more scientific and credible, but it has started to sink in that while we are interested in using what we know works, the aim is not just to prove to the world that a+b=c. Rather, it is to actually impact lives and create a continuum of positive change in the community. Thus while we work to find methods that have the greatest impact, I’m learning to celebrate every single mother’s life saved, every appropriate referral, every reported case of domestic violence and not focus solely on comparative, statistically significant change as a measure of success.
Talking to the field staff and continually hearing each individual’s story of personal growth and of helping save one life even when the figures are not enough to shift the analysis software significantly is helping shift my focus from significant statistical changes and the scientific rationale for every single step taken.
So, I’m shifting from my strict stance on evidence-based to evidence informed. I understand that it is not immediately possible to explain in great detail every method that has worked, and that impact cannot always be quantified. I’m learning to balance my desire for scientific purity and exact results, with exploring innovation, embracing the diversity of communities, and adapting my methods to create a flexible evidence-informed model. And I’m learning that seeing one changed life is evidence as credible as seeing a 70 percent change on a spreadsheet.