Social Norms: Why Women Go So Underdiagnosed

Yerlin Holguin

Co-Presenters: Individual Presentation

College: The Dorothy and George Hennings College of Science, Mathematics and Technology

Major: Computer Science

Faculty Research Mentor: Sharmistha Das Iyer

Abstract:

Article 25 of the Universal Declaration of Human Rights attests that, among other things, everyone has the right to a standard of living adequate for the health and well-being of a person and their family. This includes medical care. Nevertheless, women have accounts of medical professionals downplaying, minimizing, or disregarding their symptoms or pain. Research for diagnoses that impact both women and men is oftentimes performed solely on male medical practitioners. Because symptoms present differently in men and women, some women are late to receive the same diagnoses, if they get one at all.This research sheds light on women’s behaviors/symptoms and how their doctors might underestimate their symptoms. This mixed methods study focused on how women – especially women of color – are often late for diagnoses because of “social norms.” These social norms were racial and gender bias and the normalization of pain for women. The study asked the following questions: Does it take longer for women to receive diagnoses when they are more aware of social norms? Do women of color take longer to receive diagnoses because of doctors ignoring/not believing their symptoms? Four significant themes about women’s experiences emerge from the research: Misdiagnosis and/or delay of diagnoses; Preventability; Dismissal of symptoms; and Normalization of pain. These experiences reveal larger issues within the healthcare system. This study sheds light on the myriad reasons why women of color receive insufficient or misinformed medical care, and presents recommendations about bias training, patient-centered dialogue and treatment, and systemic changes that must occur in order for all individuals to receive effective and timely healthcare.Keywords: Healthcare; gender; race; bias; social norms

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Computational Approaches and Current Advances in Zika Virus (ZIKV) Drug and Vaccine Design