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Representation Matters: Content Analysis of Breastfeeding Images in a Commercial Stock Image Bank.

Lauren M DinourMelanie Shefchik
Published in: Journal of racial and ethnic health disparities (2024)
Several behavioral change theories posit that normative influences contribute to breastfeeding behaviors and disparities. Given that media has historically presented a narrow view of what is deemed normative in human milk feeding, this study describes who and what is represented in breastfeeding images available in a stock image bank, and whether differences exist based on the breastfeeding parent's skin color. Using content analysis, the most relevant 2% (n = 2284) of breastfeeding and lactation images in Adobe Stock were coded for 60 variables within 12 categories, such as skin color, ability, setting, skin exposure, etc. Descriptive statistics were used to characterize the sample, and the Chi-square test of independence and Mann-Whitney U test were used to compare images of breastfeeding parents with light and non-light skin color. Most images portrayed breastfeeding parents and breastfed children with light colored skin, only one child, an infant-aged child, and no other person. Scant images included accessories considered non-normative. Light skin parents were more frequently depicted with a wedding ring compared to non-light skin parents. Non-light skin parents were more often photographed outdoors compared to light skin parents. Images of light skin parents more frequently showed breast skin, whereas images of non-light skin parents more often showed nipple and/or areola skin. The paucity of diverse people and portrayals of breastfeeding in many ways mirror, and may even perpetuate, societal breastfeeding challenges and inequities. These findings highlight an immediate need for an expanded library of images showcasing a wider variety of breastfeeding experiences.
Keyphrases
  • deep learning
  • soft tissue
  • wound healing
  • preterm infants
  • convolutional neural network
  • optical coherence tomography
  • human milk
  • mental health
  • machine learning
  • healthcare
  • low birth weight