Mental health & prevention · 2025
Using network analysis to identify risk and protective factors for mental health in LGBTQ+ youth.
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Eadeh Hana-May, Rider G Nic, Lawrence Samantha E, Gower Amy L, Watson Ryan J, Ip Ka I, Eisenberg Marla E
Аннотация
OBJECTIVE: Youth with marginalized gender and sexual identities, or lesbian, gay, bisexual, transgender, queer, and/or questioning youth (LGBTQ+) are at heightened risk for mental health concerns due to oppressive social contexts. To guide clinical practice and address health disparities for LGBTQ+ youth, researchers must identify central and influential risk and protective factors that have the greatest potential to affect mental health.
METHOD: Network analysis was used to model the complex relationships between risk factors, protective factors, mental health, and personal identities (gender, sexual, and racial and ethnic identities) among LGBTQ+ youth in a large statewide cross-sectional dataset from Minnesota (N = 24,400, Mage = 14.77, SD = 1.30). Grade-stratified networks were analyzed using qgraph in RStudio with Graphical Least Absolute Shrinkage and Selection Operator model selection. Adjacency tables with correlations were used as the input data.
RESULTS: The three grade networks were similar in density and overall pattern of centrality metrics. Bias-based bullying variables, regardless of grade, and three substance use variables were central nodes across networks. School safety and empowerment were the protective factor variables with the highest expected influence values.
CONCLUSIONS: Results indicate that bias-based bullying has a significant impact on mental health and other risk factors, replicating previous studies. Implications for prevention of mental health concerns include reducing bias-based bullying and substance use, the importance of early and accurate identification of mental health concerns, and increasing protective factors early on.
METHOD: Network analysis was used to model the complex relationships between risk factors, protective factors, mental health, and personal identities (gender, sexual, and racial and ethnic identities) among LGBTQ+ youth in a large statewide cross-sectional dataset from Minnesota (N = 24,400, Mage = 14.77, SD = 1.30). Grade-stratified networks were analyzed using qgraph in RStudio with Graphical Least Absolute Shrinkage and Selection Operator model selection. Adjacency tables with correlations were used as the input data.
RESULTS: The three grade networks were similar in density and overall pattern of centrality metrics. Bias-based bullying variables, regardless of grade, and three substance use variables were central nodes across networks. School safety and empowerment were the protective factor variables with the highest expected influence values.
CONCLUSIONS: Results indicate that bias-based bullying has a significant impact on mental health and other risk factors, replicating previous studies. Implications for prevention of mental health concerns include reducing bias-based bullying and substance use, the importance of early and accurate identification of mental health concerns, and increasing protective factors early on.