Gendered Mental Health Stigma in Masked Language Models (2022)

Abstract: 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.

## introduction mental health issues are heavily stigmatized, preventing many individuals from seeking appropriate care @xcite . in addition, social psychology studies have shown that this stigma manifests differently for different genders: mental illness is more visibly associated with women, but tends to be more harshly derided in men @xcite . this asymmetrical stigma constitutes harms towards both men and women, increasing the risks of under-diagnosis or over-diagnosis respectively. figure 1 : we investigate masked language models' biases at the intersection of gender and mental health. using theoretically-motivated prompts about mental health conditions, we have models fill in the masked token, then examine the probabilities of generated words with gender associations. in this work, we propose a framework to investigate joint encoding of gender bias and mental health stigma in masked language models (mlms), which have become widely used in downstream applications @xcite . our framework uses questionnaires developed in psychology research to curate prompts about mental health conditions. then, with several selected language models, we mask out parts of these prompts and examine the model's tendency to generate explicitly gendered words, including pronouns, nouns, first names, and noun phrases. 1 in order to disentangle general gender biases from gender biases tied to mental health stigma, we compare these results with prompts describing health conditions that are not related to mental health. additionally, to understand the effects of domainspecific training data, we investigate both generalpurpose mlms and mlms pretrained on mental health corpora. we aim to answer the two research questions below. rq1: do mlms associate mental health conditions with a particular gender? to answer rq1, we curate three sets of prompts that reflect three healthcare-seeking phases: diagnosis, intention, and action, based on the widely-cited health action process approach @xcite . we prompt the models to generate the subjects of sentences that indicate someone is (1) diagnosed with a mental health condition, (2) intending to seek help or treatment for a mental health condition, and (3) taking action to get treatment for a mental health condition. we find that models associate mental health conditions more strongly with women than with men, and that this disparity is exacerbated with sentences indicating intention and action to seek treatment. however, mlms pretrained on mental health corpora reduce this gender disparity and promote gender-neutral subjects. rq2: how do mlms' embedded preconceptions of stereotypical attributes in people with mental health conditions differ across genders? to answer rq2, we create a set of prompts that describe stereotypical views of someone with a mental health condition by rephrasing questions from the attribution questionnaire (aq-27), which is widely used to evaluate mental health stigma in psychology research @xcite . then, using a recursive heuristic, we prompt the models to generate gendered phrases and compare the aggregate probabilities of different genders. we find that mlms pretrained on mental health cor-pora associate stereotypes like anger, blame, and pity more strongly with women than men, while associating avoidance and lack of help with men. our empirical results from these two research questions demonstrate that models do perpetuate harmful patterns of overlooking men's mental health and capture social stereotypes of men being less likely to receive care for mental illnesses. however, different models reduce stigma in some ways and increase it in other ways, which has significant implications for the use of nlp in mental health as well as in healthcare in general. 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 and applying these models in downstream applications. foot_1 2 background and related work mental health stigma and gender. mental health stigma can be defined as the negative perceptions of individuals based on their mental health status @xcite . this definition is implicitly composed of two pieces: assumptions about who may have mental health conditions in the first place, and assumptions about what such people are like in terms of characteristics and personality. thus, our study at the intersection of gender bias and mental health stigma is twofold: whether models associate mental health conditions with a particular gender, and what presuppositions these models have towards different genders with mental illness. multiple psychology studies have reported that mental health stigma manifests differently for different genders @xcite . regarding the first aspect of stigma, mental illness is consistently more associated with women than men. the world health organization (who) reports a greater number of mental health diagnoses in women than in men (who, 2021), but the fewer diagnoses in men does not indicate that men struggle less with mental health. rather, men are less likely to seek help and are significantly underdiagnosed, and stigma has been cited as a leading barrier to their care @xcite . regarding the second aspect of stigma, prior work in psychology has developed ways to evalu-ate specific stereotypes towards individuals with mental illness. specifically, the widely used attribution model developed by @xcite defines nine dimensions of stigma foot_2 about people with mental illness: blame, anger, pity, help, dangerousness, fear, avoidance, segregation, and coercion. the model uses a questionnaire (aq-27) to evaluate the respondent's stereotypical perceptions towards people with mental health conditions @xcite . to the best of our knowledge, no prior work has examined how these stereotypes foot_3 differ towards people with mental health conditions from different gender groups. ## methodology we develop a framework grounded in social psychology literature to measure mlms' gendered mental health biases. our core methodology centers around (1) curating mental-health-related prompts and (2) comparing the gender associations of tokens generated by the mlms. 5 in this section, we discuss methods for the two research questions introduced in § 2. ## results in this section, we discuss the main results for our two research questions. 9 comprehensive results of all statistical tests are in appendix c and e. ## conclusion our contributions in this work are threefold. first, we introduce a framework grounded in psychology research that examines models' gender biases in the context of mental health stigma. our methods of drawing from psychology surveys, examining both general and attribute-level associations (rq1 and rq2), and developing controlled comparisons are reusable in other settings of complex, intersec-tional biases. second, we present empirical results showing that mlms do perpetuate societal patterns of under-emphasizing men's mental health: models generally associate mental health with women and associate stigma dimensions like avoidance with men. this has potential impact for the use of nlp in mental health applications and healthcare more generally. third, our empirical investigation of gender and mental health stigma in several different models shows that training on domain-specific data can reduce stigma in some ways but increase it in others. our study demonstrates the complexity of measuring social biases and the importance of considering multiple dimensions.