1 - (SYM 20) Development of the Weight Bias in Healthcare Scale

Background: Weight bias (or weight stigma) can be defined as negative attitudes, beliefs, and discrimination toward individuals with higher weight (Lee et al., 2014). Weight bias within the healthcare community is pervasive (Puhl & Heuer, 2009) and has unique consequences (e.g., patient avoidance of healthcare, providers attributing all medical concerns to patients’ high weight; Alberga et al., 2019). Few instruments have been designed to assess weight bias specifically among healthcare providers, with most instruments designed for use within the general population (Lacroix et al., 2017). These measures lack sensitivity to the nuances of weight bias among healthcare providers.
Methods: The current study addresses this gap through the development and testing of psychometric properties of the Weight Bias in Healthcare Scale. This 16-item instrument was designed to assess weight bias across a range of healthcare providers and students in healthcare training programs. This instrument includes a social desirability subscale to allow for statistical control of overly favorable responding. Refinement of the instrument occurred across three phases. Initial item analyses were conducted across the first two phases (N = 112). During the third phase, an exploratory and confirmatory factor analysis of the revised instrument were conducted with a new sample of healthcare providers and students (N = 178).
Results: The scree plot and parallel analysis supported a two-factor model, though goodness-of-fit indices reflected suboptimal fit. All weight bias items loaded onto one factor and all social desirability items loaded onto a second factor.
Discussion: Overall, results from this multiphase study support the use of the Weight Bias in Healthcare Scale for quick and accurate assessment of weight bias among healthcare providers and healthcare students. This scale may aid in evaluating the efficacy of bias reduction interventions and guide their implementation in training programs and hospital settings. Future research should focus on refinement of instrument items to improve suboptimal goodness-of-fit indices.