Over 40 million adults each year in the U.S. contend with mental health issues, many of whom do not receive quality treatment (NAMI 2018; NIMH 2015). These individuals are often subjected to stigmatization: the social process within social relationships involving the devaluing of someone through ‘conferring labels and stereotyping’ (Pescosolido and Martin 2015). While stigma can take several forms, it consistently contributes to negative social, personal, and emotional outcomes for individuals with mental illness and their families (Pescosolido and Martin 2015).
My research aims to shed light on some of the key mechanisms that influence how stigmatizing beliefs are formed, sustained, or dismantled over time, so that effective strategies can be developed to reduce stigma and subsequently improve the social, health, and economic experiences of those with mental health issues and the people they associate with. This Fellowship project stems from a portion of my dissertation that investigates mental health stigma via deliberate and automatic cognition processes. Members of a culture develop cognitive associations between concepts over time that help individuals quickly navigate complex social environments and interpret social stimuli (DiMaggio 1997). These implicit cognitive associations, however, can also reify cultural stereotypes and influence discriminatory behaviors. For example, the public may implicitly associate having mental illness with ‘being violent’ and avoid them socially, despite the fact that people with mental illness account for less than 5% of gun violence in the United States (Desmarias et al. 2014; Metzl and MacLeish 2016; Pescosolido, Manago, and Monahan 2019).
During my Fellowship, I am developing network maps depicting how people implicitly associate health conditions and stigmatized concepts (e.g., the public associates ‘being violent’ and ‘being a bad co-worker’ with ‘having depression’ but not with ‘having cancer’) with the goal of juxtaposing public perceptions of stigmatized and non-stigmatized health conditions in a way that highlights negative stereotyping. These maps will be presented alongside information (1) disconfirming negative health stereotypes displayed in the visualization (e.g., people with mental illness are at greater risk of experiencing violent victimization than being violent themselves) and (2) conveying the harmful effects of mental health stigma.
Thus far, I have spent most of my time exploring various software packages to create network visualizations. While I have substantial experience with Stata, learning about various network mapping and visualization tools has been a rewarding task, albeit a challenging and time-consuming one. I have spent most of my time reading about network visualization, taking advantage of online resources, trying things out via trial and error, and brainstorming new ideas. During this process, I have been grateful for our class discussions about the feasibility of projects and the importance of flexibility with regards to setting timelines and goals. I remain hopeful that these network maps will become effective tools for teaching about and countering mental health stigma and have learned that engaging in this type of work is an important and long-term endeavor.
In the upcoming weeks, I look forward to gaining more data visualization skills while using R, as well as brainstorming ways to incorporate stereotype-disconfirming information alongside the network maps in a clear, reader-friendly style. I am sure more trial and error awaits me; hopefully I will make small steps forward along the way.