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Methods for prospectively incorporating gender into health sciences research

Published:September 23, 2020DOI:https://doi.org/10.1016/j.jclinepi.2020.08.018

      Highlights

      • Sex, a biological construct, and gender, a social construct are two distinct variables that may independently influence human health.
      • Despite calls for inclusion of sex and gender into health sciences research, gender is often ignored or conflated with sex.
      • In this commentary, we provide clarification of the distinction between these two variables and concrete examples of gender-related variables that can be collected under the four domains of gender identity, gender roles, gender relations, and institutionalized gender.
      • We also provide methods for incorporating these variables into statistical analysis.
      • We hope these guidelines will help researchers in their efforts to incorporate gender into their studies, thereby meeting requirements of funding agencies and ultimately improving health equity and precision medicine.

      Abstract

      Numerous studies have demonstrated that sex (a biological variable) and gender (a psychosocial construct) impact health and have discussed the mechanisms that may explain these relationships. Funding agencies have called for all health researchers to incorporate sex and gender into their studies; however, the way forward has been unclear to many, particularly due to the varied definition of gender. We argue that just as there is no standardized definition of gender, there can be no standardized measurement thereof. However, numerous measurable gender-related variables may influence individual or population-level health through various pathways. The initial question should guide the selection of specific gender-related variables based on their relevance to the study, to prospectively incorporate gender into research. We outline various methods to provide clarification on how to incorporate gender into the design of prospective clinical and epidemiological studies as well as methods for statistical analysis.

      Keywords

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