Equity, diversity and inclusion are core competencies in research
Analyzing research data by gender of participants is a requirement for many granting agencies, given the known drawbacks of ignoring gender differences in medical research. Despite growing awareness of these harms, sex and gender analysis is still often done incorrectly or not included at all8. Although there are guidelines for analysis based on sex and gender, inaccurate or imprecise collection of sex and gender data from study participants limits the analysis. For example, many administrative datasets only use gender and/or sex binary options. Interviewers should not assume the sex or gender of participants, but rather should familiarize themselves with best practices for asking questions about sex assigned at birth, sexual orientation and gender identity when collecting data. data. Transgender and gender-diverse people are often overlooked when designing data collection forms, which can cause them to withdraw from a study, resulting in missed associations during analysis. Reporting results using more granular categories of sex and gender can allow individual data to be pooled across studies, even when the sample size is too small to allow statistical comparison with larger groups.
Additionally, investigators may not correctly differentiate whether their variable of interest is sex, sexual orientation, gender identity, gender roles, gender expression, or gender relations.9, each of which may have different associations with health outcomes. Researchers then unintentionally use proxies rather than examining the true causal factor; for example, using race to approximate racism or gender rather than body size.
Similar issues are seen with collecting and analyzing data on race, ethnicity, ability, and other EDI-related characteristics. Researchers should consider that data produced from historically excluded groups may need to be held to a higher level of ethics and principles. This is due in part to the oppression and injustices perpetrated by scientific communities when these groups have participated in research, including forced participation in studies, withholding of results, and use of data to demean participants and justify disparities rather than to address health inequalities. . A prominent example of this prejudice is the “thrifty gene” studies conducted on Indigenous peoples in Canada in the 1990s, when researchers claimed to have identified a gene that predisposed Indigenous peoples to diabetes.5. This study has been used for decades to support the false notion that race is a biological concept and to justify Canada’s lack of action to address the social determinants of health in Indigenous communities, including racism, poverty and lack of access to healthy food.5. First Nations peoples in Canada have developed a set of guiding principles for research data — OCAP (Ownership, Control, Access and Possession) — to protect themselves from continued colonization by research.seven. These guidelines can serve as a framework for collecting and using data from marginalized groups, although researchers should also engage with other affected communities to ensure that specific cultural requirements for data are met.