Gender as a relational concept is rarely considered in epidemiology. However, an in-depth reflection on gender conceptualisation and operationalisation can advance gender analysis in quantitative health research, allowing for more valid evidence to support public health interventions. We constructed a context-specific gender score to assess how its discriminatory power differed in sub-groups defined by social positions relevant to intersectional analyses, i.e., sex/gender, race, class, age and sexual attraction.
We created a gender score with the help of multivariable logistic regression models and conditional probabilities based on gendered social practices and expressed on a masculinity-femininity continuum, using data of the German Socioeconomic Panel. With density plots, we exploratively compared distributions of gendered social practices and their variation across social groups.
We included 13 gender-related variables to define a gender score in our sample (
The distribution of gendered social practices differs among social groups, which empirically backs up the theoretical notion of gender being a context-specific construct. Economic participation and household structures remain essential drivers of heterogeneity in practices among women and men in most social positions. The gender score can be used in epidemiology to support concerted efforts to overcome these gender (in)equalities—which are important determinants of health inequalities.