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58. Women in Science '93: A "Female Style"?

Author

Keller, Linda H., Schiebinger, Londa, Proctor, Robert N., Thomas, Linda A., Lodish, Harvey F., Bleyman, Lea Kanner, Knecht, Kathryn, Elks, Marty, Farrell, Brenda, Osborne, Barbara, Hynes, Nancy, Schlick, Shulamith, and Benditt, John

Published
1993

69. Additional file 1 of Gender-related variables for health research

Author

Nielsen, Mathias W., Stefanick, Marcia L., Peragine, Diana, Neilands, Torsten B., Ioannidis, John P. A., Pilote, Louise, Prochaska, Judith J., Cullen, Mark R., Einstein, Gillian, Klinge, Ineke, LeBlanc, Hannah, Paik, Hee Young, and Schiebinger, Londa

Abstract

Additional file 1: Fig. S1. Flowchart of article inclusion and exclusion in the literature search. Fig. S2. Screeplot of the factor analysis reported in Table S8. Table S1. Item phrasing and descriptive statistics for the 44 potentially relevant gender-related items. Table S2. Response options for all 44 items included in the exploratory factor analyses. Table S3. Rank of gender characteristics based on occurrences (>2). Table S4. Search-terms for meta-analyses of existing scales measuring each gender variable. Table S5. Health-related items and response options. Table S6. Demographic items and response options. Table S7. Recoding of ten variables to allow for the largest possible sample in the EFA. Table S8. Exploratory Factor Analysis (Full factor model). Table S9. Communalities and unique variances for exploratory factor analysis presented in Table S8. Table S10. Exploratory Factor Analysis (Full factor model), Oblimin rotation. Table S11. Exploratory Factor Analysis (Full factor model), Varimax rotation. Table S12. Exploratory Factor Analysis (Full factor m odel), Equamax rotation. Table S13. Exploratory Factor Analysis (Full factor model), Quartimax rotation. Table S14. Factor loadings for CFA Models 1 and 2 in sample 1. Table S15. Factor loadings for CFA Samples 2 and 3 (Configural invariance). Table S16. Factor loadings for CFA Samples 2 and 3 (Metric invariance, 24 items). Table S17. Factor loadings for final CFA in samples 2 and 3 (Scalar invariance, 24 items). Table S18. Factor loadings for final CFA in samples 2 and 3 (Metric invariance, 25 items). Table S19. Factor loadings for final CFA samples 2 and 3 (Scalar invariance, 25 items). Table S20. Correlations between the factors in samples 1, 2 and 3. Table S21. Negative binomial regression predicting number of days with poor physical health (during past 30 days) (with gender identity as covariate). Table S22. Negative Binomial regression predicting number of days with poor mental health (during past 30 days) (with gender identity as covariate). Table S23. Negative binomial regression predicting number of days where poor mental or physical health prevented the respondent from doing usual activities (during past 30 days) (with gender identity as covariate). Table S24. Logistic regression predicting general health status (excellent, very good, good= 0, fair, poor= 1) (with gender identity as covariate). Table S25. Logistic regression predicting vaping (not vaping=0, vaping=1) (with gender identity as covariate). Table S26. Logistic regression predicting smoking (not smoking=0, smoking=1) (with gender identity as covariate). Table S27. Logistic regression predicting binge drinking (less than monthly=0, monthly, weekly, and daily=1) (with gender identity as covariate). Table S28. Logistic regression predicting overweight (BMI

Published
2021
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71. Gender-related variables for health research

Author

Nielsen, Mathias Wullum, Stefanick, Marcia, Peragine, Diana, Neilands, Torsten B., Ioannidis, John P. A., Pilote, Louise, Prochaska, Judith J., Cullen, Mark R., Einstein, Gillian, Klinge, Ineke, LeBlanc, Hanna, Paik, Hee Young, Schiebinger, Londa, Nielsen, Mathias Wullum, Stefanick, Marcia, Peragine, Diana, Neilands, Torsten B., Ioannidis, John P. A., Pilote, Louise, Prochaska, Judith J., Cullen, Mark R., Einstein, Gillian, Klinge, Ineke, LeBlanc, Hanna, Paik, Hee Young, and Schiebinger, Londa

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 at

Published
2021

76. Jeanne Baret: the first woman to circumnavigate the globe

Author

Schiebinger, Londa

Subjects
Discoveries (in geography) -- History, Women explorers -- History, Science and technology
Abstract

The voyages of scientific discovery conjure in our minds images of Sir Hans Sloane bioprospecting in Jamaica in 1687 or Joseph Banks voyaging aboard the Endeavour to Tahiti and New Zealand in 1768. But women also set foot on rickety and unsure ocean-going vessels in the 18th century in the service of science. The German-born Maria Sibylla Merian voyaged from Amsterdam to Surinato in South America in 1699 to search for exotic caterpillars. She sought one that would produce a thread to rival silk, a costly and much sought-after fabric in early-modern Europe. In the process, she produced one of the most celebrated 18th-century natural history books.

Published
2003

82. Gender-Related Variables for Health Research

Author

Nielsen, Mathias W., primary, Stefanick, Marcia L., additional, Peragine, Diana, additional, Neilands, Torsten B., additional, Ioannidis, John P. A., additional, Pilote, Louise, additional, Prochaska, Judith J., additional, Cullen, Mark R., additional, Einstein, Gillian, additional, Klinge, Ineke, additional, LeBlanc, Hannah, additional, Paik, Hee Y., additional, and Schiebinger, Londa, additional

Published
2020
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90. Sex and gender in health research: updating policy to reflect evidence

Author

Wainer, Zoe, Carcel, Cheryl, Hickey, Martha, Schiebinger, Londa, Schmiede, Annette, McKenzie, Briar, Jenkins, Christine, Webster, Jacqui, Woodward, Mark, Hehir, Angela, Solomon, Benjamin, de Costa, Caroline, Lukaszyk, Caroline, Colville, Deborah J, Dempsey, Erika, Wright, Gavin M, Mishra, Gita D, Fisher, Jane RW, Kulkarni, Jayashri, Mitchell, Julie Anne, Hutchison, Katrina, Thompson, Kelly, Jorm, Louisa, Chappell, Louise, van der Meulen, Maarinke, Henry, Amanda, DiGiacomo, Michelle, Huxley, Rachel, Ivers, Rebecca, Peters, Sanne, Rogers, Wendy A, Wang, Xia, Norton, Robyn, Wainer, Zoe, Carcel, Cheryl, Hickey, Martha, Schiebinger, Londa, Schmiede, Annette, McKenzie, Briar, Jenkins, Christine, Webster, Jacqui, Woodward, Mark, Hehir, Angela, Solomon, Benjamin, de Costa, Caroline, Lukaszyk, Caroline, Colville, Deborah J, Dempsey, Erika, Wright, Gavin M, Mishra, Gita D, Fisher, Jane RW, Kulkarni, Jayashri, Mitchell, Julie Anne, Hutchison, Katrina, Thompson, Kelly, Jorm, Louisa, Chappell, Louise, van der Meulen, Maarinke, Henry, Amanda, DiGiacomo, Michelle, Huxley, Rachel, Ivers, Rebecca, Peters, Sanne, Rogers, Wendy A, Wang, Xia, and Norton, Robyn

Published
2020

93. Changing assumptions

Author

Schiebinger, Londa

Subjects
Motherhood, the Elephant in the Laboratory: Women Scientists Speak Out (Nonfiction work) -- Monosson, Emily, Why Aren't More Women in Science?: Top Researchers Debate the Evidence (Nonfiction work) -- Ceci, Stephen J. -- Williams, Wendy M., Books -- Book reviews
Abstract

WHY AREN'T MORE WOMEN IN SCIENCE? Top Researchers Debate the Evidence. Edited by Stephen J. Ceci and Wendy M. Williams. xx + 254 pp. American Psychological Association, 2007. $59.95. MOTHERHOOD, [...]

Published
2008

95. La investigadora Londa Schiebinger exhorta a usar el análisis de género para impulsar la ciencia

Author

Schiebinger, Londa, Gabinete de Presidencia CSIC, Departamento de Comunicación CSIC, Schiebinger, Londa, Gabinete de Presidencia CSIC, and Departamento de Comunicación CSIC

Abstract

La investigadora Londa Schiebinger, catedrática de Historia de la Ciencia de la Universidad de Stanford y referente internacional en estudios de género, exhorta a usar el conocimiento que aporta el análisis de género y sexo para mejorar los métodos de investigación científica y la innovación. La historiadora suele señalar que investigar sin incluir las perspectivas de sexo y género cuesta vidas y dinero. Schiebinger impartió la conferencia: Innovaciones de género: aumentando la excelencia en la investigación y su impacto, que se celebró en la sede central del Consejo Superior de Investigaciones Científicas (CSIC), el día 18 de Noviembre. Schiebinger (Lincoln, Nebraska, Estados Unidos, 1952), impulsora del proyecto de investigación Gendered Innovations, cita diversos casos en salud, medicina, medio ambiente e ingeniería en los que el estudio de género ha mejorado los métodos científicos y ha impulsado la innovación. Además, repasa otros casos con algoritmos de aprendizaje y robótica en los que la ausencia del análisis de género ha llevado a resultados erróneos. La ponencia se enmarca en los actos de conmemoración del 80º aniversario del CSIC.

Published
2019

97. Gender Differences and Performance in Science

Author

Muller, Carol B., Ride, Sally M., Fouke, Janie, Whitney, Telle, Denton, Denice D., Cantor, Nancy, Nelson, Donna J., Plummer, Jim, Busch-Vishniac, Ilene, Meyers, Carolyn, Rosser, Sue V., Schiebinger, Londa, Roberts, Eric, Burgess, David, Beeson, Craig, Metz, Susan Staffin, Sanders, Lucinda, Watford, Bevlee A., Ivey, Elizabeth S., Fox, Mary Frank, Wettack, Sheldon, Klawe, Maria, Wulf, William A., Girgus, Joan, Leboy, Phoebe S., Babco, Eleanor L., Shanahan, Betty, Didion, Catherine, Chubin, Daryl E., Frize, Monique, Ganter, Susan L., Nalley, E. Ann, Franz, Judy, Abruña, Héctor D., Strober, Myra H., Daniels, Jane Zimmer, Carter, Emily A., Rhodes, Jean H., Schrijver, Iris, Zakian, Virginia A., Simons, Barbara, Martin, Ursula, Boaler, Jo, Jolluck, Katherine Rose, Mankekar, Purnima, Gray, Robert M., Conkey, Margaret W., Stansky, Peter, Xie, Aihua, Martin, Pino, Miller, Jo Anne, Thornton, Amelia Tess, LaPaugh, Andrea, Rhode, Deborah L., Gelpi, Barbara C., Harrold, Mary Jean, Spencer, Cherrill M., Ellis, Carla Schlatter, Lord, Susan, Quinn, Helen, Murnane, Margaret, Jones, Patricia P., Hellman, Frances, Wight, Gail, O'Hara, Ruth, Pickering, Mary, Sheppard, Sheri, Leith, David, Paytan, Adina, Sommer, Matthew H., Shafer, Audrey, Grusky, David, Yennello, Sherry, Madan, Ashima, Johnson, Denise L., Yanagisako, Sylvia, Chou-Green, Jennifer M., and Robinson, Sandra

Published
2005

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