The institutions that turned information into evidence for public policy decisions are being defunded and discredited just as generative AI floods them with fluent, unsourced text. Sifting what is known from what is claimed is now a core responsibility for public health institutions.
Achieving 'gender equality in health' has been advocated by many as a key goal in health globally; however, we observe this goal has been defined differently by different users. In this paper, we explore the question of how progress towards gender equality in health has been defined and measured, and how the selection of indicators and targets can influence perceptions of who in a population is suffering disadvantage. We summarise the common population health measures-such as life expectancy and risk exposure-and illustrate how each of these measures may lead to different conclusions about gender equality in health. We call for more specificity when defining and measuring gender inequality in health, and propose expanding the focus from 'gender inequalities' (comparison between genders) to also addressing 'within-gender inequities' (a focus that incorporates addressing inequities within different gender identity groups).