Date of Award
Doctor of Philosophy
Dr. Jonathan C. Baker
Dr. Anthony DeFulio
Dr. Jessica Frieder
Dr. Allison Ilem
Stroke recognition, signal detection, decision making, public health, decision science
The impact of stroke on the lives of individuals and the healthcare system is considerable. Damage from stroke can be reduced if the treatment is administered at the appropriate time so early recognition is essential. One problem is that strokes present in a variety of ways that sometimes do not fit into the Facial drooping, Arm weakness, Speech difficulties and Time (FAST; American Heart Association, 2019) acronym. Signal detection is one way to measure decision making under conditions of uncertainty (e.g., discriminating stroke symptoms and risk factors from other symptoms, and non-risk factors). The methodology also allows us to consider motivation or bias toward a particular decision. I examined the effects of levels of feedback on performance of a random sample of participants from Amazon Mechanical Turk (MTurk). Feedback consisted of continuous feedback delivered on every trial, or asymmetric feedback that occurred only on a percentage of trials. These two levels were compared with a non-feedback reminder condition and a control condition. I solicited the opinions of medical professionals with experience in neurology or stroke as part of a social validity survey. In general, medical professionals found the procedures to be acceptable, but thought that it was most appropriate for a high-risk population.
Bailey, Jordan D., "A Signal Detection Framework for Evaluating the Effects of Feedback on Stroke Recognition" (2020). Dissertations. 3672.