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Full-Text Articles in Medicine and Health Sciences
Covid-19 And Quantitative Literacy: Focusing On Probability, Michael A. Lewis
Covid-19 And Quantitative Literacy: Focusing On Probability, Michael A. Lewis
Numeracy
The COVID-19 pandemic is arguably the worst crisis the world has faced, so far, in this new century. We haven’t seen a pandemic like this since the 1918 Flu at the beginning of the last century, and, as of this writing, there appears to be no end in sight. What those of us who’re focused on quantitative methods have noticed, in addition to the many people dying, becoming ill, and losing their livelihoods, is the importance of quantitative literacy to an understanding of what’s going on. That’s what this article is about. Specifically, it’s about how the COVID-19 pandemic is …
Effects Of Quantitative Literacy On Healthcare Decision-Making: An Aural Context, Robert G. Root, Sonia Bhala
Effects Of Quantitative Literacy On Healthcare Decision-Making: An Aural Context, Robert G. Root, Sonia Bhala
Numeracy
We propose a relationship between sensory modality, numerical formatting, and performance on a survey simulating healthcare decision-making. We examine the current literature on aural health literacy, and specifically aural literacy coupled with health numeracy. We then create a survey instrument called the Bhala test for this purpose and demonstrate that it is moderately internally consistent and provides results that correlate with the NUMi assessment, a widely accepted measure of health numeracy. The quantitative information provided in the Bhala test has two treatments, percentage and natural frequency formats, in an effort to determine which format is easier for subjects to use …
Parts Of The Whole: Error Estimation For Science Students, Dorothy Wallace
Parts Of The Whole: Error Estimation For Science Students, Dorothy Wallace
Numeracy
It is important for science students to understand not only how to estimate error sizes in measurement data, but also to see how these errors contribute to errors in conclusions they may make about the data. Relatively small errors in measurement, errors in assumptions, and roundoff errors in computation may result in large error bounds on computed quantities of interest. In this column, we look closely at a standard method for measuring the volume of cancer tumor xenografts to see how small errors in each of these three factors may contribute to relatively large observed errors in recorded tumor volumes.