When Oysters Can Fly

A major part of my research is sectioning oyster shells. This means taking a shell that is usually hundreds of years old and slicing it in half to count the growth bands. This requires the use of a saw that is bigger than I am, and an oyster shell that is only a few inches long.

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Know it All?

Sometimes I feel discouraged because I will tell someone that I am an archaeologist and they then assume that I know everything about every site there is. People are especially interested in discussing Rome and Egypt, which are totally not in my knowledge area.

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Opportunities

I met a new student at William and Mary today. In our discussions, we stumbled upon the topic of researching with a professor. He told me that everyone he has talked to has an “in”, and he didn’t know how to get there. I told him that it was simple: ask a professor if you can do research with them.

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My Own Project: ER and preventable care admits

So I finally finished my own project. I used the same data I’ve been working with (quarterly inpatient data from Florida), but now only focusing on two types of patients: ER admits and patients with preventable conditions. Preventable conditions are diagnoses for which timely and effective ambulatory care reduces the risks of hospitalization by either preventing the onset of or managing an illness or condition. Examples include pneumonia, congestive heart failure, asthma, and immunizable conditions. As a result, preventable conditions are frequently used as a measure of health care access and so I hypothesized that during a recession there would be more of these types of conditions. ER admits are an important subgroup because hospitals must provide everyone with a minimum degree of service even if the patient has no insurance and cannot pay. The ER is the only part of a hospital that is required to treat everyone, so I hypothesized that during a recession there would be more ER admits. My dependent variables of interest are the number of ER admits, preventable condition admits, and PC through ER admits for each hospital. My explanatory variables were the county unemployment rate and county population controls and hospital and time controls. When I only looked at the effect of unemployment on ER or PC admits, the effect was not significantly different from zero. However, when I looked at patients that met both criteria, the effect was positive and significant. In other words, during recessions there are more ER admits with diseases that could have been prevented with proper health care.

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