Education and Behavior Change

Rachel Kelley
Blog for 10/21 class

To prepare for our class/guest speaker tomorrow, I checked out the TeachAIDS website and watched some of their animated videos about HIV/AIDS. I was impressed! The videos used several techniques to deliver the message effectively - repetition (or phrases and images), symbols/analogy (related the immune system to a defensive army with army commanders being the CD4 cells), an interactive format (a quiz was included), and colorful and engaging animation, just to name a few. The doctor/teacher figure in the video is authoritative, but by asking questions and praising the patient for wanting to learn, he/she also seems approachable and caring. The progression of the video also makes a lot of sense. It starts with a doctor-patient scenario, then progresses to explain why it is important learn about HIV/AIDS, how someone gets infected, how to know (or not know) if one is infected, how to protect against infections, why testing is important, and what people can do to help. I'm interested to learn more about the development of this project in class tomorrow!

These videos seem like a great tool, but as the Gregson article suggests, behavior change can be a long process that involves entire communities. The article discusses the ambiguity of the causes of HIV decline in Zimbabwe; it seems to be neither optimistic nor pessimistic about the future of the epidemic there. We have been discussing behavior change as it relates to health education in local clinics in another one of my classes, so it was interesting to read about behavior change in a related but different context. Measuring the impact of "behavior change" is even difficult here in the US because of the interpersonal/social factors that are nearly impossible to control. Also, people have different stages of willingness and ability to change, so behavior change is often a scaled rather than an either/or response.

The Gregson article concludes by listing some of the factors that might have contributed to behavior change. I'm curious to know more about the various measures, particularly which one is estimated to have had the greatest effect. Given the ambiguity of the data, however, perhaps it is difficult to make such an estimation. Also a random question - why are the discussed age groups of women (15-24)and men (17-29) different? Wouldn't this inconsistency make it harder to easily compare the two groups?