Mathias W Nielsen1, Marcia L Stefanick2, Diana Peragine3, Torsten B Neilands4, John P A Ioannidis2, Louise Pilote5, Judith J Prochaska2, Mark R Cullen6, Gillian Einstein3, Ineke Klinge7, Hannah LeBlanc8, Hee Young Paik9, Londa Schiebinger10. 1. Department of Sociology, University of Copenhagen, Øster Farimagsgade 5, Bld. 16, DK-1014, Copenhagen K, Denmark. 2. Stanford Prevention Research Center, Department of Medicine, Stanford University, 1265 Welch Rd, Stanford, CA, 94305-5411, USA. 3. Department of Psychology, University of Toronto, 100 St George St, Toronto, ON, M5S 3G3, Canada. 4. Department of Medicine, University of California, San Francisco, 550 16th Street, 3rd Floor, San Francisco, CA, 94143, USA. 5. Department of Medicine, McGill University Health Center, McGill University, 5252 De Maisonneuve Blvd, Office 2B.39, Montréal, QC, H4A 3S5, Canada. 6. Stanford Center for Population Health Sciences, Stanford University, 1701 Page Mill Road, Palo Alto, 94304, USA. 7. Horizon 2020 Advisory Group for Gender, European Commission, Brussels, Belgium. 8. Department of Science & Technology Studies, Cornell University, 303 Morrill Hall, Ithaca, NY, 14853, USA. 9. Center for Gendered Innovations in Science and Technology Research, 405 KSTC, 22 Teheranro-7gil, Gangnam-gu, Seoul, 06130, Republic of Korea. 10. History of Science, Stanford University, Building 200, 450 Jane Stanford Way, Stanford, CA, 94305, USA. schiebinger@stanford.edu.
Abstract
BACKGROUND: In this paper, we argue for Gender as a Sociocultural Variable (GASV) as a complement to Sex as a Biological Variable (SABV). Sex (biology) and gender (sociocultural behaviors and attitudes) interact to influence health and disease processes across the lifespan-which is currently playing out in the COVID-19 pandemic. This study develops a gender assessment tool-the Stanford Gender-Related Variables for Health Research-for use in clinical and population research, including large-scale health surveys involving diverse Western populations. While analyzing sex as a biological variable is widely mandated, gender as a sociocultural variable is not, largely because the field lacks quantitative tools for analyzing the influence of gender on health outcomes. METHODS: We conducted a comprehensive review of English-language measures of gender from 1975 to 2015 to identify variables across three domains: gender norms, gender-related traits, and gender relations. This yielded 11 variables tested with 44 items in three US cross-sectional survey populations: two internet-based (N = 2051; N = 2135) and a patient-research registry (N = 489), conducted between May 2017 and January 2018. RESULTS: Exploratory and confirmatory factor analyses reduced 11 constructs to 7 gender-related variables: caregiver strain, work strain, independence, risk-taking, emotional intelligence, social support, and discrimination. Regression analyses, adjusted for age, ethnicity, income, education, sex assigned at birth, and self-reported gender identity, identified associations between these gender-related variables and self-rated general health, physical and mental health, and health-risk behaviors. CONCLUSION: Our new instrument represents an important step toward developing more comprehensive and precise survey-based measures of gender in relation to health. Our questionnaire is designed to shed light on how specific gender-related behaviors and attitudes contribute to health and disease processes, irrespective of-or in addition to-biological sex and self-reported gender identity. Use of these gender-related variables in experimental studies, such as clinical trials, may also help us understand if gender factors play an important role as treatment-effect modifiers and would thus need to be further considered in treatment decision-making.
BACKGROUND: In this paper, we argue for Gender as a Sociocultural Variable (GASV) as a complement to Sex as a Biological Variable (SABV). Sex (biology) and gender (sociocultural behaviors and attitudes) interact to influence health and disease processes across the lifespan-which is currently playing out in the COVID-19 pandemic. This study develops a gender assessment tool-the Stanford Gender-Related Variables for Health Research-for use in clinical and population research, including large-scale health surveys involving diverse Western populations. While analyzing sex as a biological variable is widely mandated, gender as a sociocultural variable is not, largely because the field lacks quantitative tools for analyzing the influence of gender on health outcomes. METHODS: We conducted a comprehensive review of English-language measures of gender from 1975 to 2015 to identify variables across three domains: gender norms, gender-related traits, and gender relations. This yielded 11 variables tested with 44 items in three US cross-sectional survey populations: two internet-based (N = 2051; N = 2135) and a patient-research registry (N = 489), conducted between May 2017 and January 2018. RESULTS: Exploratory and confirmatory factor analyses reduced 11 constructs to 7 gender-related variables: caregiver strain, work strain, independence, risk-taking, emotional intelligence, social support, and discrimination. Regression analyses, adjusted for age, ethnicity, income, education, sex assigned at birth, and self-reported gender identity, identified associations between these gender-related variables and self-rated general health, physical and mental health, and health-risk behaviors. CONCLUSION: Our new instrument represents an important step toward developing more comprehensive and precise survey-based measures of gender in relation to health. Our questionnaire is designed to shed light on how specific gender-related behaviors and attitudes contribute to health and disease processes, irrespective of-or in addition to-biological sex and self-reported gender identity. Use of these gender-related variables in experimental studies, such as clinical trials, may also help us understand if gender factors play an important role as treatment-effect modifiers and would thus need to be further considered in treatment decision-making.
Entities:
Keywords:
Biomedical outcomes; Gender measures; Sex differences
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