Technology for transgender healthcare: Access, precarity & community care.

While much of the transgender health literature has focused on poor health outcomes, less research has examined how trans people find reliable information on, and actually go about accessing, gender-affirming healthcare. Through qualitative interviews with creators of trans technologies, that is, technologies designed to address problems that trans people face, we found that digital technologies have become important tools for proliferating access to gender-affirming care and related health information. We found that technologists often employed different processes for creating their technologies, but they coalesced around the goal of enabling and increasing access to gender-affirming care. Creators of trans health technologies also encountered precarious conditions for creating and maintaining their technologies, including regional gaps left by national resources focused on the US east and west coasts. Findings demonstrated that trans tech creators were motivated to create and maintain these technologies as a means of caring for one another and forming trans communities in spite of the precarious conditions trans people face living under systemic oppression.

Community-Engaged Participatory Methods to Address Lesbian, Gay, Bisexual, Transgender, Queer, and Questioning Young People's Health Information Needs With a Resource Website: Participatory Design and Development Study.

BACKGROUND: Lesbian, gay, bisexual, transgender, queer, and questioning (LGBTQ+) young people (aged 15 to 25 years) face unique health challenges and often lack resources to adequately address their health information needs related to gender and sexuality. Beyond information access issues, LGBTQ+ young people may need information resources to be designed and organized differently compared with their cisgender and heterosexual peers and, because of identity exploration, may have different information needs related to gender and sexuality than older people. OBJECTIVE: The objective of our study was to work with a community partner to develop an inclusive and comprehensive new website to address LGBTQ+ young people's health information needs. To design this resource website using a community-engaged approach, our objective required working with and incorporating content and design recommendations from young LGBTQ+ participants. METHODS: We conducted interviews (n=17) and participatory design sessions (n=11; total individual participants: n=25) with LGBTQ+ young people to understand their health information needs and elicit design recommendations for the new website. We involved our community partner in all aspects of the research and design process. RESULTS: We present participants' desired resources, health topics, and technical website features that can facilitate information seeking for LGBTQ+ young people exploring their sexuality and gender and looking for health resources. We describe how filters can allow people to find information related to intersecting marginalized identities and how dark mode can be a privacy measure to avoid unwanted identity disclosure. We reflect on our design process and situate the website development in previous critical reflections on participatory research with marginalized communities. We suggest recommendations for future LGBTQ+ health websites based on our research and design experiences and final website design, which can enable LGBTQ+ young people to access information, find the right information, and navigate identity disclosure concerns. These design recommendations include filters, a reduced number of links, conscientious choice of graphics, dark mode, and resources tailored to intersecting identities. CONCLUSIONS: Meaningful collaboration with community partners throughout the design process is vital for developing technological resources that meet community needs. We argue for community partner leadership rather than just involvement in community-based research endeavors at the intersection of human-computer interaction and health.

Coming Out to Doctors, Coming Out to "Everyone": Understanding the Average Sequence of Transgender Identity Disclosures Using Social Media Data.

Purpose: Gender transition is a complex life change, and transgender identity disclosures are pivotal moments that delineate the gender transition process. The purpose of this study was to quantify the average sequence in which transgender people disclose their transgender identity to different people in their lives, such as medical professionals, family members, and online networks, and to understand the emotional implications of these disclosures. Methods: We used mixed methods to identify 362 transgender identity disclosure social media posts within 41,066 total posts from 240 Tumblr transition blogs (online spaces in which transgender people document gender transitions). We manually assigned each disclosure post an audience category, and then calculated the average sequence in which people in this sample disclosed their transgender identity to different audiences. Results: Health professionals, such as physicians and therapists, were on average some of the very first people to whom transgender Tumblr bloggers disclosed their transgender identity. Such disclosures were often anxiety provoking and emotionally difficult, whether intentional or involuntary. Next, they often disclosed to friends, followed by close family (e.g., parents and siblings) and then extended family (e.g., grandparents). Mass disclosures to large portions of a person's network, such as on one's Facebook profile, usually came late in the disclosure process. Conclusion: Gender transition is a staged process that includes a series of disclosures to different audiences that follows an average sequence. Because health care providers (e.g., physicians and therapists) who work with transgender patients are often some of the very first people to whom transgender people in our sample disclosed, providers must practice extra sensitivity when responding to such disclosures.

Mapping gender transition sentiment patterns via social media data: toward decreasing transgender mental health disparities.

OBJECTIVE: Transgender people face substantial mental health disparities, and this population's emotional well-being can be particularly volatile during gender transition. Understanding gender transition sentiment patterns can positively impact transgender people by enabling them to anticipate, and put support in place for, particularly difficult time periods. Yet, tracking sentiment over time throughout gender transition is challenging using traditional research methods. This study's objective was to use social media data to understand average gender transition sentiment patterns. MATERIALS AND METHODS: Computational sentiment analysis and statistics were used to analyze 41 066 posts from 240 Tumblr transition blogs (online spaces where transgender people document gender transitions) to understand sentiment patterns over time and quantify relationships between transgender identity disclosures, sentiment, and social support. RESULTS: Findings suggest that sentiment increases over time on average throughout gender transition, particularly when people receive supportive responses to transgender identity disclosures. However, after disclosures to family members, people experienced temporary increased negative sentiment, followed by increased positive sentiment in the long term. After transgender identity disclosures on Facebook, an important means of mass disclosure, those with supportive networks experienced increased positive sentiment. CONCLUSIONS: With foreknowledge of sentiment patterns likely to occur during gender transition, transgender people and their mental healthcare professionals can prepare with proper support in place throughout the gender transition process. Social media are a novel data source for understanding transgender people's sentiment patterns, which can help reduce mental health disparities for this marginalized population during a particularly difficult time.

The Language of LGBTQ+ Minority Stress Experiences on Social Media.

LGBTQ+ (lesbian, gay, bisexual, transgender, queer) individuals are at significantly higher risk for mental health challenges than the general population. Social media and online communities provide avenues for LGBTQ+ individuals to have safe, candid, semi-anonymous discussions about their struggles and experiences. We study minority stress through the language of disclosures and self-experiences on the r/lgbt Reddit community. Drawing on Meyer's minority stress theory, and adopting a combined qualitative and computational approach, we make three primary contributions, 1) a theoretically grounded codebook to identify minority stressors across three types of minority stress-prejudice events, perceived stigma, and internalized LGBTphobia, 2) a machine learning classifier to scalably identify social media posts describing minority stress experiences, that achieves an AUC of 0.80, and 3) a lexicon of linguistic markers, along with their contextualization in the minority stress theory. Our results bear implications to influence public health policy and contribute to improving knowledge relating to the mental health disparities of LGBTQ+ populations. We also discuss the potential of our approach to enable designing online tools sensitive to the needs of LGBTQ+ individuals.

Supplementing Public Health Inspection via Social Media.

Foodborne illness is prevented by inspection and surveillance conducted by health departments across America. Appropriate restaurant behavior is enforced and monitored via public health inspections. However, surveillance coverage provided by state and local health departments is insufficient in preventing the rising number of foodborne illness outbreaks. To address this need for improved surveillance coverage we conducted a supplementary form of public health surveillance using social media data: Yelp.com restaurant reviews in the city of San Francisco. Yelp is a social media site where users post reviews and rate restaurants they have personally visited. Presence of keywords related to health code regulations and foodborne illness symptoms, number of restaurant reviews, number of Yelp stars, and restaurant price range were included in a model predicting a restaurant's likelihood of health code violation measured by the assigned San Francisco public health code rating. For a list of major health code violations see (S1 Table). We built the predictive model using 71,360 Yelp reviews of restaurants in the San Francisco Bay Area. The predictive model was able to predict health code violations in 78% of the restaurants receiving serious citations in our pilot study of 440 restaurants. Training and validation data sets each pulled data from 220 restaurants in San Francisco. Keyword analysis of free text within Yelp not only improved detection of high-risk restaurants, but it also served to identify specific risk factors related to health code violation. To further validate our model we applied the model generated in our pilot study to Yelp data from 1,542 restaurants in San Francisco. The model achieved 91% sensitivity 74% specificity, area under the receiver operator curve of 98%, and positive predictive value of 29% (given a substandard health code rating prevalence of 10%). When our model was applied to restaurant reviews in New York City we achieved 74% sensitivity, 54% specificity, area under the receiver operator curve of 77%, and positive predictive value of 25% (given a prevalence of 12%). Model accuracy improved when reviews ranked highest by Yelp were utilized. Our results indicate that public health surveillance can be improved by using social media data to identify restaurants at high risk for health code violation. Additionally, using highly ranked Yelp reviews improves predictive power and limits the number of reviews needed to generate prediction. Use of this approach as an adjunct to current risk ranking of restaurants prior to inspection may enhance detection of those restaurants participating in high risk practices that may have gone previously undetected. This model represents a step forward in the integration of social media into meaningful public health interventions.