Implicit and explicit anti-fat bias: The role of weight-related attitudes and beliefs Academic Article uri icon

abstract

  • Introduction The increasing prevalence of anti-fat bias in American society comes at a great cost to the health and well-being of people who are overweight or obese. A better understanding of the correlates of anti-fat bias would inform development of interventions for reducing anti-fat bias. Based on three theoretical perspectives, this study tested the relation between attitudes and beliefs about weight and anti-fat bias (implicit and explicit): (1) The belief that one is like people who are fat (social identity theory). (2) The belief that one can control her/his weight (attribution theory). And (3) the beliefs that most people prefer thin people and that weight is important (socio-cultural theory). Methods Participants were 66,799 volunteers (47,265 women, mean age of 27.88 ± 11.9 years) who completed the Thin-Fat Implicit Association Test on the Project Implicit website (https://implicit.harvard.edu/) during 2016. Explicit anti-fat bias and weight-related attitudes and beliefs were assessed by self-report. Correlation and regression analyses were conducted to examine links between weight-related attitudes and beliefs and anti-fat bias. Results All tested weight-related attitudes and beliefs were significantly (p < .001) correlated with explicit and implicit anti-fat bias, but some of the correlations were very weak. An examination of the relative contribution of the tested weight-related attitudes and beliefs to a model explaining anti-fat bias suggested that the strongest correlates of explicit anti-fat bias were the beliefs that weight was important (β = 0.194, p < .001), that most people prefer thin people (β = 0.177, p < .001), and that the respondent was like people who are fat (β = −0.180, p < .001). Discussion The social-identity and socio-cultural theories may provide a stronger explanation for anti-fat bias relative to attribution theory. Future research could use longitudinal designs with more reliable measures in order to verify these cross-sectional findings.

publication date

  • January 1, 2018