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Gender-based linguistic differences in letters of recommendation for rhinology fellowship over time: A dual-institutional follow-up study using natural language processing and deep learning.

Vikram VasanChristopher P ChengShaun EdalatiShreya MandloiDavid K LernerAnthony Del SignoreMadeleine SchabergSatish GovindarajMindy RabinowitzGurston NyquistAlfred Marc Iloreta
Published in: International forum of allergy & rhinology (2024)
This follow-up dual-institutional and longitudinal study further evaluated for underlying gender biases in LORs for rhinology fellowship. Explicit and implicit linguistic gender bias was found, heavily favoring male applicants.
Keyphrases
  • deep learning
  • mental health
  • autism spectrum disorder
  • artificial intelligence
  • emergency medicine