Background A high risk of mental health or substance addiction issues among sexual and gender minority populations may have more nuanced characteristics that may not be easily discovered by traditional statistical methods. Objective This review aims to identify literature studies that used machine learning (ML) to investigate mental health or substance use concerns among the lesbian, gay, bisexual, transgender, queer or questioning, and two-spirit (LGBTQ2S+) population and direct future research in this field. Methods The MEDLINE, Embase, PubMed, CINAHL Plus, PsycINFO, IEEE Xplore, and Summon databases were searched from November to December 2020. We included original studies that used ML to explore mental health or substance use among the LGBTQ2S+ population and excluded studies of genomics and pharmacokinetics. Two independent reviewers reviewed all papers and extracted data on general study findings, model development, and discussion of the study findings. Results We included 11 studies in this review, of which 81% (9/11) were on mental health and 18% (2/11) were on substance use concerns. All studies were published within the last 2 years, and most were conducted in the United States. Among mutually nonexclusive population categories, sexual minority men were the most commonly studied subgroup (5/11, 45%), whereas sexual minority women were studied the least (2/11, 18%). Studies were categorized into 3 major domains: web content analysis (6/11, 54%), prediction modeling (4/11, 36%), and imaging studies (1/11, 9%). Conclusions ML is a promising tool for capturing and analyzing hidden data on mental health and substance use concerns among the LGBTQ2S+ population. In addition to conducting more research on sexual minority women, different mental health and substance use problems, as well as outcomes and future research should explore newer environments, data sources, and intersections with various social determinants of health.
Background: Youth who are lesbian, gay, bisexual, trans, queer, 2-spirit, and of other identities (LGBTQ2S+) experience mental health disparities and higher rates of substance use when compared to their cisgender and heterosexual peers and yet also experience more barriers to access to services. The purpose of this paper is to determine the types of mental health and substance use programs and services exclusive to LGBTQ2S+ youth in Ontario during the pandemic. Methods: An environmental scan was conducted to identify existing programs and services in Ontario, Canada that offered exclusive mental health and addiction services to LGBTQ2S+ individuals aged 16–29, either by offering services to all or subgroups within the population. Organizations, services and programs were classified by the geographical distribution of services, populations served, types of programming or services, methods of service delivery, and program criteria. Results: In total, 113 organizations and 240 programs and services were identified as providing mental health and substance use services exclusively to LGBTQ2S+ youth. Identified adaptations for the COVID-19 pandemic included cancelling in-person services, increasing online and telephone services, and expansion to province wide from local availability. Conclusions: The findings highlight the importance of offering services that provide culturally inclusive care for LGBTQ2S+ youth, and these results can also be used by policy makers to inform policies. In particular, there was a lack of culturally relevant clinical services for youth requiring a greater intensity of treatment.
BACKGROUND People at high risk of mental health or substance addiction issues among sexual and gender minorities may have more nuanced characteristics that may not be easily discovered by traditional statistical methods. OBJECTIVE This review aimed at identifying literature that used machine learning to investigate mental health or substance use concerns among lesbian, gay, bisexual, transgender, queer or questioning and two-spirit (LGBTQ2S+) population as well as directing future research in this field. METHODS MEDLINE, EMBASE, PubMed, CINAHL Plus, PsycINFO and IEEE Xplore, Summon databases were searched from November to December 2020. We included original studies which used machine learning to explore mental health and/or substance use among LGBTQ2S+ population and excluded studies of genomics and pharmacokinetics. Two independent reviewers reviewed all papers and extracted data on general study findings, model development and discussion of study findings. RESULTS We included 11 studies in this review, of which 9 (82%) studies were on mental health and only 2 (18%) studies were on substance use concerns. All studies were published within last 2 years and majority were conducted in the Unites States. Among mutually non-exclusive population categories, sexual minorities male were the most commonly studied subgroup (n=5, 45%), while sexual minorities female were studied the least (n=2, 18%). Studies were categorized into 3 major domains: online content analysis (n=6, 55%), prediction modelling (n=4, 36%) and imaging study (n=1, 9%). CONCLUSIONS Machine learning can be a promising tool of capturing and analyzing hidden data of mental health and substance use concerns among LGBTQ2S+ people. In addition to conducting more research on sexual minority women, different mental health and substance use problems as well as outcomes, future research should explore newer environments and data sources and intersections with various social determinants of health.
Background: LGBTQ2S youth experience mental health disparities and higher rates of substance use when compared to their cisgender and heterosexual peers yet also experience more barriers to access to services. The purpose of this environmental scan is to determine the types of mental health and substance use programs and services exclusive to LGBTQ2S youth in Ontario and how these services have changed during the pandemic. Methods: This environmental scan was conducted to identify existing programs and services in Ontario that offered exclusive mental health and addiction services to LGBTQ2S+ individuals aged 16-29, either by offering services to all or subgroups within the population. Results: In total, 113 organizations and 240 programs and services were identified as providing mental health and substance use services exclusively to LGBTQ2S+ youth. Four main themes were identified from the scan, including the distribution of services, types of services, methods of service delivery and program criteria. Adaptations for the COVID pandemic included cancelling in-person services, increasing online and telephone services, and expansion to province-wide from local availability. Conclusions: The findings from this scan highlight the importance of offering services that provide culturally inclusive care for LGBTQ2S+ youth, and these results can also be used by policy makers to inform policies. In particular, there was a lack of culturally-relevant clinical services for youth requiring greater intensity of treatment.
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