Individuals who identify as sexual and gender minorities, including lesbian, gay, bisexual, transgender, queer, and others (LGBTQ+) are more likely to experience poorer health than their heterosexual and cisgender counterparts. One primary source that drives these health disparities is minority stress (i.e., chronic and social stressors unique to LGBTQ+ communities' experiences adapting to the dominant culture). This stress is frequently expressed in LGBTQ+ users' posts on social media platforms. However, these expressions are not just straightforward manifestations of minority stress. They involve linguistic complexity (e.g., idiom or lexical diversity), rendering them challenging for many traditional natural language processing methods to detect. In this work, we designed a hybrid model using Graph Neural Networks (GNN) and Bidirectional Encoder Representations from Transformers (BERT), a pre-trained deep language model to improve the classification performance of minority stress detection. We experimented with our model on a benchmark social media dataset for minority stress detection (LGBTQ+ MiSSoM+). The dataset is comprised of 5,789 human-annotated Reddit posts from LGBTQ+ subreddits. Our approach enables the extraction of hidden linguistic nuances through pretraining on a vast amount of raw data, while also engaging in transductive learning to jointly develop representations for both labeled training data and unlabeled test data. The RoBERTa-GCN model achieved an accuracy of 0.86 and an F1 score of 0.86, surpassing the performance of other baseline models in predicting LGBTQ+ minority stress. Improved prediction of minority stress expressions on social media could lead to digital health interventions to improve the wellbeing of LGBTQ+ people-a community with high rates of stress-sensitive health problems.

Minority stress is the leading theoretical construct for understanding LGBTQ+ health disparities. As such, there is an urgent need to develop innovative policies and technologies to reduce minority stress. To spur technological innovation, we created the largest labeled datasets on minority stress using natural language from subreddits related to sexual and gender minority people. A team of mental health clinicians, LGBTQ+ health experts, and computer scientists developed two datasets: (1) the publicly available LGBTQ+ Minority Stress on Social Media (MiSSoM) dataset and (2) the advanced request-only version of the dataset, LGBTQ+ MiSSoM+. Both datasets have seven labels related to minority stress, including an overall composite label and six sublabels. LGBTQ+ MiSSoM (N = 27,709) includes both human- and machine-annotated la-bels and comes preprocessed with features (e.g., topic models, psycholinguistic attributes, sentiment, clinical keywords, word embeddings, n-grams, lexicons). LGBTQ+ MiSSoM+ includes all the characteristics of the open-access dataset, but also includes the original Reddit text and sentence-level labeling for a subset of posts (N = 5,772). Benchmark supervised machine learning analyses revealed that features of the LGBTQ+ MiSSoM datasets can predict overall minority stress quite well (F1 = 0.869). Benchmark performance metrics yielded in the prediction of the other labels, namely prejudiced events (F1 = 0.942), expected rejection (F1 = 0.964), internalized stigma (F1 = 0.952), identity concealment (F1 = 0.971), gender dysphoria (F1 = 0.947), and minority coping (F1 = 0.917), were excellent. Descriptive analyses, ethical considerations, limitations, and possible use cases are provided.

There has been growing attention toward including people with lived and living experience (PWLLE) with substance use, substance use disorders, and recovery in public-facing activities. The goals of including PWLLE in sharing their perspectives often include demonstrating that recovery is possible, destigmatizing and humanizing people who have substance use experiences, and leveraging their lived experience to illuminate a particular topic or issue. Recently, the National Council for Mental Wellbeing issued a set of guidelines entitled, “Protecting Individuals with Lived Experience in Public Disclosure,” which included a “Lived Experience Safeguard Scale.” We offer the present commentary to bolster some of the ideas presented by the Council and to articulate suggested changes to this guidance, with the goal of reducing unintentional gatekeeping and stigma. Specifically, we offer that there are numerous problems with the recommendation to only invite people who have “five or more years of sustained recovery” to contribute to public discourse. The idea of perceived stability after five years of abstinence is not new to us or the field. We suggest that this idea excludes people who have experienced the present rapidly changing substance use landscape, people who have briefly returned to use, some young people, and people with living experience who also can valuably contribute to public discourse. We offer alternative guidelines to the National Council for Mental Wellbeing and others seeking to promote practices that are inclusive to the diversity of PWLLE.

Homelessness disproportionately impacts sexual and gender minority (SGM) people, however, few studies have examined factors that predict homelessness among SGM adults. The present secondary analysis of a survey of SGM adults living in the Southeastern U.S. in 2016 (N = 427) assessed factors associated with report of past or current homelessness, which was endorsed by 17% of participants. Congruent with our hypotheses, past socioeconomic status (SES), current SES, mental health, and race were all significant predictors and accounted for 50.5% of variance in homelessness in this population. Discrimination and housing access difficulties did not account for a significant portion of variance over and above these factors. Factors contributing to poverty and psychological distress are needed to address housing disparities for SGM adults. Discrimination factors may have become more salient since 2016. Future research is warranted to better support SGM individuals, particularly those living in the South.

Scholars suggest that marginalized people in non-urban areas experience higher distress levels and fewer psychosocial resources than in urban areas. Researchers have yet to test whether precise proximity to urban centers is associated with mental health for marginalized populations. We recruited 1733 people who reported living in 45 different countries. Participants entered their home locations and completed measures of anxiety, depression, social support, and resilience. Regression and thematic analyses were used to determine what role distance from legislative and urban centers may play in mental health when marginalized people were disaggregated. Greater distance from legislative center predicted higher anxiety and resilience. Greater distance from urban center also predicted more resilience. Thematic analyses yielded five categories (e.g., safety, connection) that further illustrated the impact of geographic location on health. Implications for community mental health are discussed including the need to better understand and further expand resilience in rural areas.

Background The optimal treatment for gender dysphoria is medical intervention, but many transgender and nonbinary people face significant treatment barriers when seeking help for gender dysphoria. When untreated, gender dysphoria is associated with depression, anxiety, suicidality, and substance misuse. Technology-delivered interventions for transgender and nonbinary people can be used discretely, safely, and flexibly, thereby reducing treatment barriers and increasing access to psychological interventions to manage distress that accompanies gender dysphoria. Technology-delivered interventions are beginning to incorporate machine learning (ML) and natural language processing (NLP) to automate intervention components and tailor intervention content. A critical step in using ML and NLP in technology-delivered interventions is demonstrating how accurately these methods model clinical constructs. Objective This study aimed to determine the preliminary effectiveness of modeling gender dysphoria with ML and NLP, using transgender and nonbinary people’s social media data. Methods Overall, 6 ML models and 949 NLP-generated independent variables were used to model gender dysphoria from the text data of 1573 Reddit (Reddit Inc) posts created on transgender- and nonbinary-specific web-based forums. After developing a codebook grounded in clinical science, a research team of clinicians and students experienced in working with transgender and nonbinary clients used qualitative content analysis to determine whether gender dysphoria was present in each Reddit post (ie, the dependent variable). NLP (eg, n-grams, Linguistic Inquiry and Word Count, word embedding, sentiment, and transfer learning) was used to transform the linguistic content of each post into predictors for ML algorithms. A k-fold cross-validation was performed. Hyperparameters were tuned with random search. Feature selection was performed to demonstrate the relative importance of each NLP-generated independent variable in predicting gender dysphoria. Misclassified posts were analyzed to improve future modeling of gender dysphoria. Results Results indicated that a supervised ML algorithm (ie, optimized extreme gradient boosting [XGBoost]) modeled gender dysphoria with a high degree of accuracy (0.84), precision (0.83), and speed (1.23 seconds). Of the NLP-generated independent variables, Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) clinical keywords (eg, dysphoria and disorder) were most predictive of gender dysphoria. Misclassifications of gender dysphoria were common in posts that expressed uncertainty, featured a stressful experience unrelated to gender dysphoria, were incorrectly coded, expressed insufficient linguistic markers of gender dysphoria, described past experiences of gender dysphoria, showed evidence of identity exploration, expressed aspects of human sexuality unrelated to gender dysphoria, described socially based gender dysphoria, expressed strong affective or cognitive reactions unrelated to gender dysphoria, or discussed body image. Conclusions Findings suggest that ML- and NLP-based models of gender dysphoria have significant potential to be integrated into technology-delivered interventions. The results contribute to the growing evidence on the importance of incorporating ML and NLP designs in clinical science, especially when studying marginalized populations.

Background: Gay, bisexual, and other sexual minority men have expressed the acceptability of patient portals as tools for supporting HIV prevention behaviors, including facilitating disclosure of HIV and other sexually transmitted infection (STI/HIV) laboratory test results to sex partners. However, these studies, in which Black or African American sexual minority men were undersampled, failed to determine the relationship of reported history of discussing HIV results with sex partners and anticipated willingness to disclose web-based STI/HIV test results using a patient portal.Objective: Among a sample of predominantly Black sexual minority men, this study aimed to (1) determine preferences for patient portal use for HIV prevention and (2) test the associations between reported history of discussing HIV results and anticipated willingness to disclose web-based STI/HIV test results with most recent main and nonmain partners using patient portals.Methods: Data come from audio-computer self-assisted interview survey data collected during the 3-month visit of a longitudinal cohort study. Univariate analysis assessed patient portal preferences by measuring the valuation rankings of several portal features. Multiple Poisson regression models with robust error variance determined the associations between history of discussing HIV results and willingness to disclose those results using web-based portals by partner type, and to examine criterion validity of the enhancing dyadic communication (EDC) scale to anticipated willingness.Results: Of the 245 participants, 71% (n=174) were Black and 22% (n=53) were White. Most participants indicated a willingness to share web-based STI/HIV test results with their most recent main partner. Slightly fewer, nonetheless a majority, indicated a willingness to share web-based test results with their most recent nonmain partner. All but 2 patient portal features were valued as high or moderately high priority by >80% of participants. Specifically, tools to help manage HIV (n=183, 75%) and information about pre-and postexposure prophylaxis (both 71%, n=173 and n=175, respectively) were the top-valuated features to include in patient portals for HIV prevention. Discussing HIV test results was significantly associated with increased prevalence of willingness to disclose web-based test results with main (adjusted prevalence ratio [aPR] 1.46, 95% CI 1.21-1.75) and nonmain partners (aPR 1.54, 95% CI 1.23-1.93).Conclusions: Our findings indicate what features Black sexual minority men envision may be included in the patient portal's design to optimize HIV prevention, further supporting the criterion validity of the EDC scale. Efforts should be made to support Black sexual minority men's willingness to disclose STI/HIV testing history and status with partners overall as it is associated significantly with a willingness to disclose testing results digitally via patient portals. Future studies should consider discussion behaviors regarding past HIV test results with partners when tailoring interventions that leverage patient portals in disclosure events.

Social well-being is one of three components of the World Health Organization's definition of health. Yet, little is known about social well-being among people living with HIV, particularly Black sexual minority men living with HIV. Extant research suggests both intrapsychic (e.g., identity integration, internalized stigma) and interpersonal (e.g., community connectedness) factors may influence social well-being. In this study, we utilize data gathered from Black sexual minority men living with HIV residing in Los Angeles County (N=102) to identify factors associated with social well-being in this population. We also tested two mediation models wherein the significant relationship between internalized homophobia and social well-being detected in our sample is mediated by LGBT community connectedness and racial, gender, and sexual identity integration. Greater social well-being was associated with lower internalized homophobia, higher LGBT community connectedness, and a greater sense of racial, gender, and sexual identity integration (p<0.01 for all). Mediation analyses revealed that LGBT community connectedness and identity integration fully mediated the effect of internalized homophobia on social well-being (p<0.01 for both), adjusting for sociodemographic characteristics. Our results suggest (a) efforts to reduce internalized homophobia may increase social well-being among Black sexual minority men living with HIV and (b) promoting identity integration and interpersonal connection may be useful approaches to foster social well-being among members of this population who experience internalized homophobia. Future research could attempt to replicate these results from our cross-sectional study using a longitudinal study design and with data gathered from participants in other (e.g., rural) contexts.

ObjectiveGeographic location can affect access to appropriate, affirming mental health care for sexual and gender minority (SGM) individuals, especially for those living in rural settings. Minimal research has examined barriers to mental health care for SGM communities in the southeastern United States. The objective of this study was to identify and characterize perceived barriers to obtaining mental health care for SGM individuals living in an underserved geographic area.MethodsDrawing from a health needs survey of SGM communities in Georgia and South Carolina, 62 participants provided qualitative responses describing barriers they encountered to accessing mental health care when needed in the previous year. Four coders used a grounded theory approach to identify themes and summarize the data.ResultsThree themes of barriers to care emerged: personal resource barriers, personal intrinsic factors, and healthcare system barriers. Participants described barriers that can inhibit access to mental health care regardless of one's sexual orientation or gender identity, such as finances or lack of knowledge about services, but several of the identified barriers intersect with SGM-related stigma or may be magnified by participants' location in an underserved region of the southeastern United States.ConclusionsSGM individuals living in Georgia and South Carolina endorsed several barriers to receiving mental health services. Personal resource and intrinsic barriers were the most common, but healthcare system barriers were present as well. Some participants described simultaneously encountering multiple barriers, illustrating that these factors can interact in complex ways to affect SGM individuals' mental health help seeking.

This study models counseling research as a social action process highlighting multicultural counselor identity. Seven co-researcher/participants engaged in a community-based reflexive contemplative practice group which aimed at dismantling the power imbalance that normally exists between researchers and participants, and to remain cognizant of the insidious nature of white supremacy. The data collected represents the content and process reflections on participating in this group which invited contemplation about identity on many different levels. Several themes emerged from data as implications for counseling research. Considering research as caring on clinical practice and future research is also discussed.

LGBTQ+ minority stress is a pervasive form of anti-LGBTQ+ adverse events and psychological strain that drives health inequities among LGBTQ+ people. Minority stress is also linguistically sophisticated (e.g., composed of cultural idioms, psycholinguistic permutations, and lexical density). Because minority stress is a linguistically sophisticated social determinant of health disparities, it is challenging to detect using natural language processing (NLP). Using 5,789 human-annotated Reddit posts from the LGBTQ+ Minority Stress on Social Media (MiSSoM+) Dataset, we investigated and compared the performance of four neural networks and two traditional machine learning architectures in modeling minority stress at both the factor (i.e., separate components of minority stress) and composite level. A novel hybrid model combining Bidirectional Encoder Representations from Transformers and convolutional neural network (BERT-CNN) improved the prediction of composite minority stress (F1 = 0.84). Our experiments on separate factors of minority stress are the first to demonstrate that hybrid neural network models can detect semantically complex expressions of prejudiced events (F1 = 0.87), expected rejection (F1 = 0.92), internalized stigma (F1 = 0.91), identity concealment (F1 = 0.92), and minority coping (F1 = 0.84). We also substantially improved the prediction of gender dysphoria (F1 = 0.94)—a conceptually new candidate component of minority stress. Big data analytics may not be a panacea for the problem of minority stress, but our work joins a growing literature base to show that deep learning models are remarkable in detecting linguistically sophisticated social determinants of health disparities in big data, thus providing evidence in support of the potential benefit from the innovative use of such technology in eliminating group-specific health inequities.

BACKGROUND:Compared to heterosexual and cisgender people, lesbian, gay, bisexual, transgender, queer, and other sexual and gender minority (LGBTQ+) people are more likely to develop problems with high-risk polysubstance use. According to syndemic theory, this disparity in high-risk polysubstance use is produced by the LGBTQ+ community's increased vulnerability to experiencing psychosocial (e.g., discrimination, unwanted sex) and structural (e.g., food insecurity, homelessness) conditions, greater likelihood of coping with concurrent health problems (e.g., human immunodeficiency virus [HIV]), and decreased opportunities to develop protective factors (e.g., social support, resilience).METHODS:Data from 306 LGBTQ+ participants living in the United States (U.S.) with a lifetime history of alcohol and drug use were analyzed; 21.2% reported lifetime problems with 10 different drugs. Bootstrapped hierarchical multiple regression was used to test demographic correlates and syndemic predictors of high-risk polysubstance use. One-way ANOVA and post-hoc comparison tests were used to test subgroup differences by gender.RESULTS:Results indicated that income, food insecurity, sexual orientation-based discrimination, and social support were associated with high-risk polysubstance use, explaining 43.9% of the variance of high-risk polysubstance use. Age, race, unwanted sex, gender identity-based discrimination, and resilience were not significant. Group comparison tests revealed that, compared to nonbinary people and cisgender sexual minority men and women, transgender individuals experienced significantly higher levels of high-risk polysubstance use and sexual orientation-based discrimination but significantly lower levels of homelessness and social support.CONCLUSION:This study provided further evidence for conceptualizing polysubstance use as an adverse outcome of syndemic conditions. Harm reduction strategies, anti-discrimination laws, and gender-affirming residential treatment options should be considered in U.S. drug policy. Clinical implications include targeting syndemic conditions to reduce high-risk polysubstance use among LGBTQ+ people who use drugs.

First-generation college students” (FGCS) are at risk for suffering from mental health issues, which have direct implication for their retention and academic success. Past investigators consistently find that college students are more likely to discuss mental health issues with their peers than with college personnel. The first step in peer-to-peer mental health support is screening students on campus to gain a baseline understanding of how they respond when encountering a peer in mental distress. The Mental Distress Response Scale (MDRS) is a screening tool for appraising students” responses to a peer in mental distress. Score validation is a crucial step, as FGCS are a unique college student population and the psychometric properties of instrumentation can vary between different populations. Psychometric testing yielded support for the validity of scores on the MDRS with FGCS. Findings have direct implications for aiding student affairs officials” universal screening efforts to support FGCS” mental health and retention.

Objective: First-generation community college students face unique risks for mental health distress, which can place them at risk for attrition and a myriad of other negative consequences. The aim of the present quantitative investigation was to test the utility of the REDFLAGS model, a mental health literacy based tool for supporting mental wellness, with a national sample of first-generation community college students. Method: Confirmatory factor analysis (CFA), logistic regression analysis, and a factorial analysis of variance (ANOVA) were computed to test the utility of the REDFLAGS model as a tool for promoting first-generation community college students' mental health. Results: The CFA demonstrated that the dimensionality of the REDFLAGS model was estimated adequately with first-generation community college students. First-generation community college students' recognition of the REDFLAGS as warning signs for mental distress emerged as a significant positive predictor of making a peer-to-peer referral to the counseling center. The factorial ANOVA revealed that first-generation community college students who were members of a Greek Organization were more likely to identify the REDFLAGS as warning signs for mental distress. Contributions: Previous investigators established multiple strategies for supporting the mental health needs of either first-generation or community college students. First-generation community college student mental health, however, has received little attention. This study demonstrates the utility of the REDFLAGS model with first-generation community college students. Considering the dearth of literature on first-generation community college student mental health, the REDFLAGS model offers novel implications for promoting the mental health needs of first-generation students enrolled in community colleges.

Quantitative research literacy, including matching variables with the appropriate statistical tests, is a key element in counselor education and preparation. Counselor educators are tasked with teaching quantitative research and statistics, which counselors-in-training tend to find anxiety-producing. Authors aimed to provide a succinct overview of matching variables with appropriate statistical tests and provide strategies counselor educators can use to enhance their pedagogy.

The Mental Distress Response Scale and Promoting Peer-to-Peer Mental Health Support: Implications for College Counselors and Student Affairs Officials Michael T. Kalkbrenner (bio) and Ryan E. Flinn (bio) Enrollment in postsecondary institutions of higher education increased 33% between 2000 and 2014 and is projected to grow another 13% between 2014 and 2025 (Hussar & Bailey, 2017). This new wave of college students is presenting with increased mental health concerns; Auerbach et al. (2016) found that approximately one fifth of college students reported clinically significant symptoms of mental health disorders (MHDs) in a 12-month period, with 16.4% not receiving any kind of treatment. If left untreated, the consequences of mental health disorders, including poor academic performance, higher attrition rates, lower retention rates, self-harm, and suicide or homicide in the most severe cases, can be severe and wide-ranging for students and for the larger campus community (Kalkbrenner & Carlisle, 2019). In response, college counselors, student affairs officials, wellness coordinators, and administrators across the United States are engaging in outreach, education, and consultation by training students to recognize and refer peers in mental distress (Kalkbrenner & Carlisle, 2019); however, in a recent national survey (N = 51,294), Albright and Schwartz (2017) found that the majority of college students (72%) did not refer a peer in psychological distress to mental health support services. The literature is lacking a psychometrically validated measure to help college counselors and student affairs officials identify how likely a student is to respond when encountering a peer in mental distress and which responses are more likely. The purpose of this study was to design, validate, and cross-validate scores on such a measure, the Mental Distress Response Scale (MDRS). The following research questions were addressed: (a) What is the underlying dimensionality of the MDRS with a large sample of undergraduate students? (b) Is the emergent factor structure of the MDRS confirmed with a new sample of undergraduate students? METHOD Because college counselors and student affairs officials are likely to have more opportunities to administer a shorter measure (e.g., during new student orientations or in classes), we sought to develop a brief screening tool of approximately 8 to 15 items. We initially generated 25 items based on the guidelines of DeVellis (2016), approximately three times as many as the final scale. The MDRS items were sent to 3 expert reviewers who had more than 70 years of combined experience in counselor education, clinical supervision, student affairs, and college counseling. Based on the reviewers’ feedback 12 items were removed. The remaining 13 items were then administered as a pilot test with a sample of 34 undergraduate students. Participants responded to a prompt asking what they would do if they encountered a [End Page 246] student who was struggling with a mental health issue. A Likert-type scale was used, based on the recommendations of DeVellis, for attitudinal measures with the following anchors: 1 (I would not do this), 2 (I would probably not do this), 3 (I’m not sure if this is something I would do), 4 (I would probably do this), or 5 (I would do this). Participants and Procedures Data from this study were purposefully collected from 2 separate universities, in different geographic locations and with diverse groups of students, both to increase the generalizability of the data set and to ensure a sufficient sample size for psychometric testing. The first university was a large, public, research-intensive university in a metropolitan area. The second university was a large, public, land-grant, Hispanic-serving institution in a rural area. They are both nationally ranked for ethnic diversity. An identical nonprobability sampling procedure, using paper copies of the questionnaire, was used to collect data from participants in the student union at both universities. For the exploratory factor analysis (EFA), data were collected from 569 respondents, 3 of whom were removed from the data set due to missing data, resulting in a total sample of 566. The demographic profile is as follows: for gender, 57.4% (n = 325) of participants identified as female, 41.9% (n = 237) male, 0.5% (n = 3) nonbinary or third gender, and 0.2% (n = 1) did not specify their gender. For...

The creation of trauma-sensitive schools has become an increasingly central focus of school reform and urban school reform in particular. While these initiatives have been framed as social justice imperatives, this framing warrants critique. This critical analysis surveys the history, models, and documented outcomes of trauma-sensitive approaches in K-12 education. Special attention is given to gaps in existing frameworks which lead to a decontextualized understanding of disproportionality, an apolitical conception of schools, and a deficit orientation toward families and communities. The authors advocate for the integration of social justice education into frameworks for trauma-sensitive schools as an initial step toward addressing these issues.