Bias in the mirror: Breaking bias without breaking ourselves

Bias is everywhere. Politicians are talking about it, corporations are trying to eradicate it and people are dying because of it. In contrast to explicit biases such as obvious racism or sexism, implicit biases exist outside our awareness and influence us despite our best intentions. This session will start with introduction to the concept of implicit bias, and its relevance to science education. Next, Dr. Sukhera describes a framework for recognizing and managing biases that has relevance for individuals and organizations. Through striving for our ideals while accepting our shortcomings we can reflect on our biases, change our behaviour and co-create change within society. At the end of this session participants will be able to: 1. Understand the topic of implicit bias and its relevance to communication in science education. 2. Describe a framework for implicit bias recognition and management for educational professionals 3. Be inspired to apply findings on the science of implicit bias towards organizational and societal chang

Leveraging Machine Learning to Understand How Emotions Influence Equity Related Education: Quasi-Experimental Study

Background: Teaching and learning about topics such as bias are challenging due to the emotional nature of bias-related discourse. However, emotions can be challenging to study in health professions education for numerous reasons. With the emergence of machine learning and natural language processing, sentiment analysis (SA) has the potential to bridge the gap. Objective: To improve our understanding of the role of emotions in bias-related discourse, we developed and conducted a SA of bias-related discourse among health professionals. Methods: We conducted a 2-stage quasi-experimental study. First, we developed a SA (algorithm) within an existing archive of interviews with health professionals about bias. SA refers to a mechanism of analysis that evaluates the sentiment of textual data by assigning scores to textual components and calculating and assigning a sentiment value to the text. Next, we applied our SA algorithm to an archive of social media discourse on Twitter that contained equity-related hashtags to compare sentiment among health professionals and the general population. Results: When tested on the initial archive, our SA algorithm was highly accurate compared to human scoring of sentiment. An analysis of bias-related social media discourse demonstrated that health professional tweets (n=555) were less neutral than the general population (n=6680) when discussing social issues on professionally associated accounts (x2 [2, n=555)]=35.455; P\u3c.001), suggesting that health professionals attach more sentiment to their posts on Twitter than seen in the general population. Conclusions: The finding that health professionals are more likely to show and convey emotions regarding equity-related issues on social media has implications for teaching and learning about sensitive topics related to health professions education. Such emotions must therefore be considered in the design, delivery, and evaluation of equity and bias-related education

Perfectionism, Power, and Process: What We Must Address to Dismantle Mental Health Stigma in Medical Education

In this commentary, the authors draw on 2 personal accounts of mental illness published by Kirk J. Brower, MD, and Darrell G. Kirch, MD, in this issue to consider how and why mental health stigma is maintained in medical education. In particular, they explore how perfectionism, power differentials, and structural forces drive mental illness stigma in medical education. They argue that mental health stigma in medical education, while deeply embedded in the physician archetype and medical culture, is not inevitable and that dismantling it will require individual courage, interpersonal acceptance, and institutional action

Disruption and Dissonance: Exploring Constructive Tensions Within Research in Medical Education

The academic medicine community has experienced an unprecedented level of disruption in recent years. In this context, the authors consider how the disruptions have impacted the state of research in medical education (RIME). The articles in this year\u27s RIME supplement reflect several constructive tensions that provide insight on future for the field. In this commentary, the authors discuss themes and propose a framework for the future. Recommendations include: normalizing help seeking during times of disruption and uncertainty, contextualizing the application of complex approaches to assessment, advancing and problematizing innovation, and recognizing the deeply embedded and systemic nature of inequities

Implicit Bias in Health Professions: From Recognition to Transformation

Implicit bias recognition and management curricula are offered as an increasingly popular solution to address health disparities and advance equity. Despite growth in the field, approaches to implicit bias instruction are varied and have mixed results. The concept of implicit bias recognition and management is relatively nascent, and discussions related to implicit bias have also evoked critique and controversy. In addition, challenges related to assessment, faculty development, and resistant learners are emerging in the literature. In this context, the authors have reframed implicit bias recognition and management curricula as unique forms of transformative learning that raise critical consciousness in both individuals and clinical learning environments. The authors have proposed transformative learning theory (TLT) as a guide for implementing educational strategies related to implicit bias in health professions. When viewed through the lens of TLT, curricula to recognize and manage implicit biases are positioned as a tool to advance social justice