ArticleBMC public health2025
Non-inclusive language in human subjects questionnaires: addressing racial, ethnic, heteronormative, and gender bias.
Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundQuestionnaires for research that involve diverse populations require inclusive language. There are few guidelines to assist researchers in minimizing social and cultural biases in data collection materials; such biases can result in harm and negatively impact data integrity.
methodsWe describe an approach to evaluating language in data collection forms reflecting racial, ethnic, heteronormative, and gender bias using the Environmental influences on Child Health Outcomes (ECHO)-wide Cohort Study (EWC) as a case study. The 245 data collection forms were used by 69 cohorts in the first seven years of the (ECHO)-wide Cohort Study (EWC). A diverse panel of reviewers (n = 5) rated all forms; each form also was rated by a second student. Items identified as reflecting bias were coded as to the specificity of the bias using nine categories (e.g., racial bias, heteronormative assumptions) following whole panel discussion. We provide recommendations for conducting inclusive research to the scientific community.
resultsThirty-six percent (n = 88) of the data collection forms were identified as containing biased language. In total, 137 instances of bias were recorded, eight instances of racial or ethnic bias, 56 instances of bias related to sex, gender identity and sexual orientation and 73 instances of bias related to universal assumptions. Seventy-three percent (n = 64) of forms with biased language are validated measures. The review culminated in recommended revisions to forms used by ECHO and the general scientific community.
conclusionAdverse health outcomes disproportionately affect marginalized populations. Utilizing culturally and socially conscious research materials that are inclusive of various identities and experiences is necessary to help remediate these disparities. Our review finds compelling evidence of bias in many widely used data collection instruments. Recommendations for conducting more inclusive science are discussed.
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