Use of Computational Methods to Enhance Technology-Delivered Interventions for Sexual and Gender Minority Health: A Scoping Review
Creators: name: Cascalheira, Cory J
Publisher: name: OSF Registries
Year: 2023
Subjects: subject: Clinical Psychology
subject: Counseling Psychology
subject: Health Psychology
subject: Social and Behavioral Sciences
subject: Psychology
subject: FOS: Psychology
subject: LGBTQ
subject: digital health
subject: ehealth
subject: gender minority
subject: human-computer interaction
subject: mhealth
subject: sexual minority

description: Compared to heterosexual people, sexual and gender minority (SGM; e.g., lesbian, gay, bisexual, transgender, queer, and others [LGBTQ+]) people experience more deleterious mental, sexual, and behavioral health outcomes (e.g., Bränström &amp; Pachankis, 2018; Centers for Disease Control and Prevention, 2021; Kidd et al., 2018; McCabe et al., 2022; Medley et al., 2016; Ross et al., 2018). Traditional LGBTQ+-affirming interventions (Craig et al., 2021; Livingston et al., 2020; McGeough &amp; Cohen, 2022; Pachankis et al., 2020), or interventions that involve individual and/or group treatment with trained professionals, have been developed to target disparities in SGM health. Despite the efficacy of LGBTQ+-affirming interventions (i.e., culturally tailored, evidence-based treatment techniques that target the distinct life experiences of LGBTQ+ people; Craig et al., 2021; Pachankis et al., 2019), many LGBTQ+ people in the United States (U.S.) lack access to affirming treatment (Cascalheira et al., 2022; Cohn &amp; Hastings, 2010; D’Augelli, 2006; Helminen et al., 2022; Paley, 2020). For example, over the last 10 years, the availability of LGBTQ+-affirming interventions across the U.S. has not improved, with only 20% of mental health treatment facilities offering such services (Cascalheira et al., 2022). Poor access to LGBTQ+-affirming interventions is especially pronounced in low-resource and high-stigma U.S. areas (Helminen et al., 2022; Pachankis et al., 2020). Moreover, consistent evidence shows that LGBTQ+ people face substantial treatment barriers (e.g., provider discrimination; Dahlhamer et al., 2016; Nguyen et al., 2018; Romanelli &amp; Hudson, 2017). Given the health disparities experienced by this underserved group, increasing access to LGBTQ+-affirming interventions across the U.S. by overcoming treatment barriers is an important public health priority. LGBTQ+-affirming technology-delivered interventions (TDIs) hold promise in increasing access to care (Schueller et al., 2019; Whaibeh et al., 2020). TDIs (e.g., mobile health apps) also exhibit encouraging acceptability evidence (for a review, see Gilbey et al., 2020). However, efficacy and scalability of LGBTQ+-affirming TDIs remain unknown (Schueller et al., 2019) because extant TDIs are limited by lack of engagement (i.e., client motivation to use TDIs) and lack of personalization (i.e., tailoring TDIs to individual client concerns; Baños et al., 2022; Scholten &amp; Granic, 2019). Computational social science (CSS) methods, such as machine learning (ML; i.e., algorithms that improve statistical relationships with more data; Raschka et al., 2022) and natural language processing (NLP; i.e., algorithms that understand patterns in human language; Bird et al., 2019), are poised to address these significant gaps (Balcombe &amp; De Leo, 2022). Indeed, research suggests that CSS (also referred to a computational) methods personalize and motivate use of TDIs (Balcombe &amp; De Leo, 2022). Nonetheless, CSS-enhanced TDIs—or TDIs that leverage computational methods to improve intervention delivery in some way, such as increasing personalization and motivations—are an emerging field of research with very little known about how these technologies are used to improve SGM mental, sexual, and behavioral health. A contemporary, systematic mapping of how CSS is used to enhance TDIs for mental, sexual, and behavioral health in SGM adults and adolescents would advance applied research with this population and may inform basic research on CSS-enhanced TDIs.