Information and communication technology-based health interventions for transgender people: A scoping review

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.

Health literacy of trans and gender diverse individuals -a cross sectional survey in Germany

INTRODUCTION

To date, there has been little research on the general health literacy of trans and gender diverse individuals, even though previous research undermines the importance of good health literacy in this sample. The aim of the article is therefore to describe the general health literacy of trans and gender diverse individuals based on a German survey.

METHODS

In September 2022, a survey study was conducted in which health literacy was recorded using HLS-EU-16. Data will be presented descriptively; gender differences will be explored using a Χ2- test and a univariate analysis of variance (ANOVA).

RESULTS

Out of N = 223 participants, n = 129 individuals (57.8%) identified as non-binary; n = 49 (22.0%) identified themselves as male, while n = 45 (20.2%) identified as female. Mean age was 28.03 years. Overall, 26.4% of all the participants showed an inadequate health literacy, as proposed by the HLS-EU-16. In trend, health-related task related to media use were more often perceived as easy compared to the German general population.

CONCLUSION

Individuals, who identify as trans and gender diverse may have a general health literacy below average compared to the German general population. However, tasks related to media use were perceived as easy, which might be a good starting point for health literacy related interventions.

TRIAL REGISTRATION

DRKS00026249, Date of registration: 15/03/2022.

Models of Gender Dysphoria Using Social Media Data for Use in Technology-Delivered Interventions: Machine Learning and Natural Language Processing Validation Study

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.