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1. Gendered Mental Health Stigma in Masked Language Models

   Lin, Inna; Njoo, Lucille; Field, Anjalie; Sharma, Ashish; Reinecke, Katharina; Althoff, Tim; Tsvetkov, Yulia (December 2022, Empirical Methods in Natural Language)

   Mental health stigma prevents many individuals from receiving the appropriate care, and social psychology studies have shown that mental health tends to be overlooked in men. In this work, we investigate gendered mental health stigma in masked language models. In doing so, we operationalize mental health stigma by developing a framework grounded in psychology research: we use clinical psychology literature to curate prompts, then evaluate the models’ propensity to generate gendered words. We find that masked language models capture societal stigma about gender in mental health: models are consistently more likely to predict female subjects than male in sentences about having a mental health condition (32% vs. 19%), and this disparity is exacerbated for sentences that indicate treatment-seeking behavior. Furthermore, we find that different models capture dimensions of stigma differently for men and women, associating stereotypes like anger, blame, and pity more with women with mental health conditions than with men. In showing the complex nuances of models’ gendered mental health stigma, we demonstrate that context and overlapping dimensions of identity are important considerations when assessing computational models’ social biases. 

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2. Gendered Mental Health Stigma in Masked Language Models

   Inna Wanyin Lin; Lucille Njoo; Anjalie Field; Ashish Sharma; Katharina Reinecke; Tim Althoff; Yulia Tsvetkov (January 2022, Conference on Empirical Methods in Natural Language Processing)

   Mental health stigma prevents many individuals from receiving the appropriate care, and social psychology studies have shown that mental health tends to be overlooked in men. In this work, we investigate gendered mental health stigma in masked language models. In doing so, we operationalize mental health stigma by developing a framework grounded in psychology research: we use clinical psychology literature to curate prompts, then evaluate the models’ propensity to generate gendered words. We find that masked language models capture societal stigma about gender in mental health: models are consistently more likely to predict female subjects than male in sentences about having a mental health condition (32% vs. 19%), and this disparity is exacerbated for sentences that indicate treatment-seeking behavior. Furthermore, we find that different models capture dimensions of stigma differently for men and women, associating stereotypes like anger, blame, and pity more with women with mental health conditions than with men. In showing the complex nuances of models’ gendered mental health stigma, we demonstrate that context and overlapping dimensions of identity are important considerations when assessing computational models’ social biases. 

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3. Women drive efforts to highlight concealable stigmatized identities in U.S. academic science and engineering

   https://doi.org/10.1371/journal.pone.0287795  

   Busch, Carly A.; Cooper, Katelyn M.; Brownell, Sara E. (July 2023, PLOS ONE)

   Fontanesi, Lilybeth (Ed.)

   Concealable stigmatized identities (CSIs) are hidden identities that carry negative stereotypes and can result in a loss of social status if revealed. Instructors often choose to conceal these CSIs due to anticipated negative student reactions, yet revealing CSIs can have a positive impact on undergraduates. Women are generally more likely to reveal personal aspects about themselves in social situations, but may face greater consequences for revealing a stigmatized identity to students given their already marginalized position in academic science and engineering. Therefore, in this study, we were interested in understanding to what extent there are differences between men and women science and engineering instructors in (i) the representation of CSIs, (ii) their decisions to reveal CSIs to undergraduates in their classes, and (iii) their perceived stigma of CSIs. Based on a national survey of over 2,000 instructors in science and engineering from very high research activity doctoral institutions, we found that women were more likely than men to report having depression, anxiety, or a disability. Of instructors who held CSIs, women had 1.5x higher odds than men of revealing their CSIs to some undergraduates compared to no undergraduates and perceived greater stigma associated with all CSIs. Despite perceiving greater stigma associated with concealable stigmatized identities, women are more likely to reveal their CSIs to college science and engineering students, leading the way to a more diverse and inclusive scientific community by demonstrating themselves as role models for these identities. 

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4. Transfer Stigma: Development of a Multi–Dimensional Scale for Community College Transfer Students

   https://doi.org/10.1080/10668926.2024.2368493  

   Chen, Yu; Liu, Jingwen (June 2024, Community College Journal of Research and Practice)

   Transfer stigma refers to a type of stigma associated with students’ transfer status and/or their community college background. It plays a significant role in post-transfer adjustment and may negatively impact post-transfer outcomes such as retention and obtaining a baccalaureate degree. This study focused on quantitatively measuring transfer stigma among transfer students attending a four-year institution. Drawing from previous studies, we developed a 13-item transfer stigma measure and included it in a transfer student survey. The survey data was collected from 450 current transfer students at a public, flagship four-year university in Louisiana. Through an exploratory factor analysis and a confirmatory factor analysis, we revealed a four-factor structure of transfer stigma measures: internalized self-stigma, perceptions about community colleges, lack of support, and perceived judgment. Subsequent statistical analyses examined group differences in the degree of transfer stigma across various transfer student subgroups defined by transfer type, gender, race/ethnicity, age, and major. 

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5. Mental Health Stigma across Diverse Genders in Large Language Models

   Njoo, Lucille; Janzen-Morel, Lee; Lin, Inna_Wanyin; Tsvetkov, Yulia (March 2024, Machine Learning for Cognitive and Mental Health Workshop (ML4CMH), AAAI 2024)

   Mental health stigma manifests differently for different genders, often being more associated with women and overlooked with men. Prior work in NLP has shown that gendered mental health stigmas are captured in large language models (LLMs). However, in the last year, LLMs have changed drastically: newer, generative models not only require different methods for measuring bias, but they also have become widely popular in society, interacting with millions of users and increasing the stakes of perpetuating gendered mental health stereotypes. In this paper, we examine gendered mental health stigma in GPT3.5-Turbo, the model that powers OpenAI’s popular ChatGPT. Building off of prior work, we conduct both quantitative and qualitative analyses to measure GPT3.5-Turbo’s bias between binary genders, as well as to explore its behavior around non-binary genders, in conversations about mental health. We find that, though GPT3.5-Turbo refrains from explicitly assuming gender, it still contains implicit gender biases when asked to complete sentences about mental health, consistently preferring female names over male names. Additionally, though GPT3.5-Turbo shows awareness of the nuances of non-binary people’s experiences, it often over-fixates on non-binary gender identities in free-response prompts. Our preliminary results demonstrate that while modern generative LLMs contain safeguards against blatant gender biases and have progressed in their inclusiveness of non-binary identities, they still implicitly encode gendered mental health stigma, and thus risk perpetuating harmful stereotypes in mental health contexts. 

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