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The Health Physics Society's 'Ask-The-Expert' feature: widening public support through empathy and science.

Emily A CaffreyJarvis Caffrey
Published in: Journal of radiological protection : official journal of the Society for Radiological Protection (2021)
A very large segment of the population is fearful of radiation, and sometimes rightly so. It is a word that conjures up images of something dangerous and invisible, and is often associated with the real fears of nuclear apocalypse that permeated the cold war era. TV dramas such as HBO's Chernobyl certainly fuel that fear response. Public response to radiological events, ranging from true emergencies such as the Fukushima Daiichi nuclear power plant accident in 2011 to benign events such as the bucket of uranium ore discovered at the Grand Canyon Visitor's Center in early 2019, highlight the need for effective public communication strategies. All too frequently when an event is not considered dangerous by scientists, we fail to capitalise on the opportunity for public engagement. Public communication and empathy are some of the most important challenges that the health physics and radiation protection community face today. Empathy is of particular importance in effective public communication- understanding and explaining the science in layman's terms is insufficient to widen public support. Rather, the ability to plainly explain the science must be coupled with an understanding of what the public or an individual is feeling about a particular issue. This requires more than science. This paper presents the Health Physics Society's Ask The Expert (ATE) feature, focuses on how ATE works, why it has been successful at building a culture of empathy, how ATE is adapting to the ever-changing public information consumption practices, and how the underlying principles ATE uses can be applied by the health physics community at large.
Keyphrases
  • healthcare
  • mental health
  • public health
  • health information
  • machine learning
  • deep learning
  • emergency department
  • adverse drug
  • optical coherence tomography
  • convolutional neural network
  • health promotion