Natural Frequency Tree vs. Conditional Probability Training for Medical Students
Study Overview
Medical students often find it hard to interpret test results, which can result in poor healthcare decisions. This study compares two training methods: Natural Frequency Tree-based Training (NF-TT) and Conditional Probability Formula-based Training (CP-FT). The goal is to see which method helps students better estimate the predictive value of medical tests.
Methods
We conducted a randomized controlled trial with medical students from two schools in South Korea. Students were randomly assigned to watch either an NF-TT or CP-FT video. Both videos were 15 minutes long. The NF-TT video used a tree structure to explain how to estimate test values, while the CP-FT video showed how to use a formula. We assessed students’ accuracy in estimating predictive values before training, right after training, and one month later.
Results
Overall, NF-TT did not show a significant improvement over CP-FT in accuracy immediately after training or at follow-up. However, for students without prior training, NF-TT was more effective in improving estimation accuracy and transfer of learning at follow-up.
Conclusions
Introducing NF-TT early in medical education could be beneficial, especially before students learn about formulas.
Clinical Trial Registration
This trial is registered with the Korea Disease Control and Prevention Agency. You can find the full trial protocol here.
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