Implicit Organizational Bias: Mental Health Treatment Culture and Norms as Barriers to Engaging with Diversity
Introduction BIPOC communities face many structural barriers to accessing mental health care. To reduce this health disparity and better serve multicultural populations, many providers are turning to person-centered care. Person-centered care is intended to improve quality of care by centering the patient’s values, preferences, and goals in collaboratively designed care plans. Although this approach has…
Read MoreA decade of studying implicit racial/ethnic bias in healthcare providers using the implicit association test
Introduction Existing research indicates that Black, Indigenous, and people of color have worse health outcomes than white people, including incidence, prevalence, severity of disease at diagnosis, rates of mortality, and lower quality of care, despite efforts to close these gaps. The health disparity gap begins at birth and persists throughout one’s life course. Though these…
Read MorePhysician-patient racial concordance and disparities in birthing mortality for newborns
Introduction Prior research has documented that sharing demographic characteristics increases a sense of understanding or empathy between two people. This in-group bias has been found to influence the decisions made by leadership teams, inspection enforcers, teachers, and judges to favor those with shared racial or gender traits (further exasperated by white racial power). Greenwood, Hardeman,…
Read MoreStructural competency: Theorizing a new medical engagement with stigma and inequality
Introduction Medical professionals recognize that physicians must learn both the science of medicine and the art of patient communication. Currently, much of the medical field is focused on the concept of “cultural competency” and “cultural humility.” These concepts have pushed medical education to move beyond “colorblindness” and recognize that social factors, such as race, ethnicity,…
Read MoreDissecting racial bias in an algorithm used to manage the health of populations
Introduction Obermeyer et al. note both the growing attention to potential racial and gender biases within algorithms and the difficulty of obtaining access to real world algorithms – including the raw data used to design and train them – in order to understand how and why bias could appear in them. This study is important…
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