Food categorizations among low-income women living in three different urban contexts: The pile sorting method

Appetite. 2019 May 1:136:173-183. doi: 10.1016/j.appet.2019.01.022. Epub 2019 Jan 31.

Abstract

Transformations in eating practices are reflected in the multiplicity of competing food-related discourses. These discourses contribute to different food categorizations among individuals. Scientists have long argued that food categorizations may help understanding cultural systems of health beliefs. However, not enough work has been conducted to improve the understanding of the dimensions of food categorizations and their interface with food choices, tastes, and culturally defined food systems. This study aims at describing and interpreting how low-income women living in three urban settings in Santos, Brazil, classify and give meaning to foods. We used the pile sorting method to investigate categorizations created by 90 women, following 6 steps: (1) creating units of analysis, (2) sorting the units of analysis into piles, (3) running multidimensional scaling analysis, (4) running cluster analyses on the multidimensional scaling coordinates, (5) labelling the clusters, and (6) analyzing consensus among the participants. The final solution to food categorizations comprised six clusters, namely: home meals, convenience foods, special meals, fish, breads and cereals, and hot dogs. Additionally, we observed four rationales for food categorization: frequency of consumption, degree of healthfulness, personal taste, and meals in which the food was usually part of. These categories highlight the importance of considering personal taste and the type of meal that the food is culturally consumed in, to propose meaningful interventions and appropriate education tools, towards promoting healthy eating practices, especially among vulnerable populations.

Keywords: Brazil; Eating practices; Food categorization; Low-income; Pile sorting; Women.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Brazil
  • Bread / statistics & numerical data
  • Cluster Analysis
  • Edible Grain
  • Fast Foods / statistics & numerical data
  • Feeding Behavior / psychology*
  • Female
  • Food Preferences / psychology*
  • Health Knowledge, Attitudes, Practice*
  • Humans
  • Meals
  • Meat Products / statistics & numerical data
  • Middle Aged
  • Poverty / statistics & numerical data*
  • Seafood / statistics & numerical data
  • Socioeconomic Factors
  • Urban Population / statistics & numerical data*
  • Young Adult