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The State of the Science of Nurses' Implicit Bias: A Call to Go Beyond the Face of the Other and Revisit the Ethics of Belonging and Power.

Holly WeiZula PriceKara EvansAmanda HaberstrohVicki Hines-MartinCandace C Harrington
Published in: ANS. Advances in nursing science (2023)
This article summarizes the current state of nurses' implicit bias and discusses the phenomenon from Levinas' face of the Other and ethics of belonging, Watson's human caring and unitary caring science, and Chinn's peace and power theory. Nurses' implicit bias is a global issue; the primary sources of nurses' implicit bias include race/ethnicity, sexuality, health conditions, age, mental health status, and substance use disorders. The current research stays at the descriptive level and addresses implicit bias at the individual level. This article invites nurses to go beyond "the face of the Other" and revisit the ethics of belonging and power.
Keyphrases
  • mental health
  • public health
  • healthcare
  • big data
  • endothelial cells
  • global health
  • risk assessment
  • machine learning
  • health information
  • deep learning
  • human health