At the Intersections: Examining Trends in Experiences of Violence, Mental Health Status, and Suicidal Risk Behaviors Among US High School Students Using Intersectionality, National Youth Risk Behavior Survey, 2015–2019

Journal of Adolescent Health(2022)

引用 4|浏览8
暂无评分
摘要
PURPOSE:Surveillance data are used for public health action, but the practice of analyzing data by single demographic characteristics may produce findings that reflect abstract categories rather than a person's lived experience. Intersectionality is a theoretical framework that advocates for individuals to be recognized as the whole of their identity and within context of power structures. Using the national Youth Risk Behavior Survey 2015-2019, we examined 5-year trends in experiencing violence, poor mental health, and suicidal risk behavior among US high school students using intersections of race/ethnicity and sex. METHODS:We used SUDAAN to calculate prevalence estimates and logistic regression models to assess for linear trends while accounting for the weighting and complex survey design. RESULTS:Among all students in aggregate, experiencing dating violence decreased while being threatened with a weapon at school and feeling persistently sad or hopeless increased over time; however, these trends did not apply to most students when stratified by identity. The one near-universal experience was that students in aggregate and almost all identities had an increased trend of skipping school because they felt unsafe there. DISCUSSION:By focusing on identities defined by two main drivers of health disparities-race/ethnicity and sex-we found that changes in risk behaviors did not occur equally among students and that prevalence estimates were highest among Black males, Black females, and Hispanic females. We outlined the power structures that frame the current educational environment. Patterns of health disparities can be highlighted by analyzing surveillance data through an intersectional lens.
更多
查看译文
关键词
Adolescents,Intersectionality,Survey,School health
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要