Dismantling Structural Stigma Related to Mental Health and Substance Use: An Educational Framework

This article was originally published here

Acad Med. 2021 Oct 12. doi: 10.1097/ACM.0000000000004451. Online ahead of print.


Stigma related to mental health and substance use (MHSU) is a well-established construct that describes how inequitable health outcomes can result from prejudice, discrimination, and marginalization. Although there is a body of literature on educational approaches to reduce stigma, anti-stigma education for MHSU has primarily focused on stigma at the social, interpersonal/public, and personal (self-stigma) levels, with little attention to the problem of structural stigma. Structural stigma refers to how inequity is manifested through rules, policies, and procedures embedded within organizations and society at large. Structural stigma is also prominent within clinical learning environments and can be transmitted through role modeling, resulting in inequitable treatment of vulnerable patient populations. Addressing structural stigma through education, therefore, has the potential to improve equity and enhance care. A promising educational approach for addressing structural stigma is structural competency, which aims to enhance health professionals’ ability to recognize and respond to social and structural determinants that produce or maintain health disparities. In this article, the authors propose a framework for addressing structural MHSU stigma in health professions education that has 4 key components and is rooted in structural humility: recognizing structural forms of stigma; reflecting critically on one’s own assumptions, values, and biases; reframing language away from stereotyping toward empathic terms; and responding with actions that actively dismantle structural MHSU stigma. The authors propose evidence-informed and practical suggestions on how structural competency may be applied within clinical learning environments to dismantle structural MHSU stigma in organizations and society at large.

PMID:34647920 | DOI:10.1097/ACM.0000000000004451