What self-medication data revealed about student health
Designing the self-medication study end-to-end taught me that determinants rarely live in one variable. Cost, access and prior experience interacted in ways a single regression coefficient couldn't tell — so I let the analysis map behaviour, not just measure it.
My undergraduate research asked a deceptively simple question: how and why do students at USIU-Africa self-medicate? I built the proposal, designed the instrument, collected the responses and ran the analysis in R. Owning the whole pipeline meant I couldn't hide behind any single step.
Determinants don't live alone
I went in expecting a tidy set of predictors. What I found was interaction. Cost shaped behaviour differently depending on proximity to a pharmacy. Prior experience with a drug mattered more for some symptoms than others. A single regression coefficient could report an association, but it couldn't narrate the behaviour — and behaviour was the point.
So I let the analysis map rather than merely measure. Cross-tabulations, stratified models and honest visualisations did more to explain self-medication than any one p-value. The goal shifted from proving an effect to describing a pattern a health-behaviour policy could actually respond to.
Evidence that speaks to policy
The most useful finding wasn't a number — it was a story the numbers supported: students self-medicate when the formal system feels slow, costly or distant. That framing is something a campus health office can act on, and it is the version of the result I care most about.
What self-medication data revealed about student health
Designing the self-medication study end-to-end taught me that determinants rarely live in one variable. Cost, access and prior experience interacted in ways a single regression coefficient couldn't tell — so I let the analysis map behaviour, not just measure it.
Interested in this work? Catherine is open to public health & data roles worldwide.
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