Queering E-Therapy: Considerations for the Delivery of Virtual Reality Based Mental Health Solutions with LGBTQ2IA+ Communities

Bolesnikov, Adrian; Golshan, Aryan; Tierney, Lauren A.; Mann, Ashi; Kang, Jin U.; Girouard, Audrey

doi:10.1007/978-3-030-99194-4_13

Cited by 3 publications

(2 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It probably took six months to get someone’ (20s, White, HIV‐negative, Montreal). Some participants further described that these services remained inaccessible due to financial costs—an issue that existed long before the pandemic (Bolesnikov et al., 2022; Gaspar et al., 2021). The dynamic relation between humans (GBQM, service providers) and non‐humans (virtual care technologies, COVID‐19 pandemic, health‐care systems and public health restrictions) generated disruptions and exacerbated long‐existing barriers to health‐care access for some GBQM who greatly needed mental and sexual health services.…”

Section: Resultsmentioning

confidence: 99%

‘It’s not as good as the face‐to‐face contact’: A sociomaterialist analysis of the use of virtual care among Canadian gay, bisexual and queer men during the COVID‐19 pandemic

Daroya,

Grey,

Klassen

et al. 2023

Sociology Health & Illness

View full text Add to dashboard Cite

The COVID‐19 pandemic led to the widespread adoption of virtual care—the use of communication technologies to receive health care at home. We explored the differential impacts of the rapid transition to virtual care during the COVID‐19 pandemic on health‐care access and delivery for gay, bisexual and queer men (GBQM), a population that disproportionately experiences sexual and mental health disparities in Canada. Adopting a sociomaterial theoretical perspective, we analysed 93 semi‐structured interviews with GBQM (n = 93) in Montreal, Toronto and Vancouver, Canada, conducted between November 2020 and February 2021 (n = 42) and June‐October 2021 (n = 51). We focused on explicating how the dynamic relations of humans and non‐humans in everyday virtual care practices have opened or foreclosed different care capacities for GBQM. Our analysis revealed that the rapid expansion and implementation of virtual care during the COVID‐19 pandemic enacted disruptions and challenges while providing benefits to health‐care access among some GBQM. Further, virtual care required participants to change their sociomaterial practices to receive health care effectively, including learning new ways of communicating with providers. Our sociomaterial analysis provides a framework that helps identify what works and what needs to be improved when delivering virtual care to meet the health needs of GBQM and other diverse populations.

show abstract

Section: Resultsmentioning

confidence: 99%

‘It’s not as good as the face‐to‐face contact’: A sociomaterialist analysis of the use of virtual care among Canadian gay, bisexual and queer men during the COVID‐19 pandemic

Daroya,

Grey,

Klassen

et al. 2023

Sociology Health & Illness

View full text Add to dashboard Cite

show abstract

“…Alternatively, transmitting social media data to the cloud might expand access to computational power, but data loss in transmits could result in significant data availability bias [ 72 ]. Second, control and customization are important in designing technology-delivered interventions for this population [ 73 ]. Researchers are encouraged to think carefully about which social media data (eg, which websites and what type of content) would be used if the ML and NLP models could run on limited text data, and what automated intervention content might be impaired if a TNB user decides to turn off data sharing capabilities.…”

Section: Discussionmentioning

confidence: 99%

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

Cascalheira¹,

Flinn²,

Zhao³

et al. 2023

JMIR Form Res

View full text Add to dashboard Cite

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.

show abstract

Setting the Game Agenda: Reviewing the Emerging Literature on Video Gaming and Psychological Well-Being of Sexual and Gender Diverse Youth

et al. 2023

View full text Add to dashboard Cite

Video gaming is a popular youth pastime that has prompted scholarship into its relationship with psychological well-being. However, sexual and gender diverse youth (SGDY) who play video games are largely overlooked in this research. SGDY experience significant mental health challenges, but utilize coping strategies mediated by digital technologies, necessitating an examination of their video game playing and its effects on well-being. This literature review synthesizes the emerging evidence base by identifying key constructs related to SGDY well-being and video gaming. Five themes were derived from the literature: (a) SGDY identity development and self-expression in video games; (b) SGDY video gaming and coping skills; (c) Social support in SGDY video gaming communities; (d) SGDY digital microaggressions in video gaming; and (e) SGDY civic engagement through video gaming. The findings establish multiple risks and opportunities for harnessing video games to support SGDY's well-being. Recommendations for practice, research, and industry collaborations are presented.

show abstract

Queering E-Therapy: Considerations for the Delivery of Virtual Reality Based Mental Health Solutions with LGBTQ2IA+ Communities

Cited by 3 publications

References 48 publications

‘It’s not as good as the face‐to‐face contact’: A sociomaterialist analysis of the use of virtual care among Canadian gay, bisexual and queer men during the COVID‐19 pandemic

‘It’s not as good as the face‐to‐face contact’: A sociomaterialist analysis of the use of virtual care among Canadian gay, bisexual and queer men during the COVID‐19 pandemic

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

Setting the Game Agenda: Reviewing the Emerging Literature on Video Gaming and Psychological Well-Being of Sexual and Gender Diverse Youth

Contact Info

Product

Resources

About