Universal and Selective Interventions to Prevent Poor Mental Health Outcomes in Young People: Systematic Review and Meta-analysis

HARVARD REVIEW OF PSYCHIATRY(2021)

引用 23|浏览3
暂无评分
摘要
Background Much is not known about the efficacy of interventions to prevent poor mental health outcomes in young people by targeting either the general population (universal prevention) or asymptomatic individuals with high risk of developing a mental disorder (selective prevention). Methods We conducted a PRISMA/MOOSE-compliant systematic review and meta-analysis of Web of Science to identify studies comparing post-test efficacy (effect size [ES]; Hedges' g) of universal or selective interventions for poor mental health outcomes versus control groups, in samples with mean age <35 years (PROSPERO: CRD42018102143). Measurements included random-effects models, I-2 statistics, publication bias, meta-regression, sensitivity analyses, quality assessments, number needed to treat, and population impact number. Results 295 articles (447,206 individuals; mean age = 15.4) appraising 17 poor mental health outcomes were included. Compared to control conditions, universal and selective interventions improved (in descending magnitude order) interpersonal violence, general psychological distress, alcohol use, anxiety features, affective symptoms, other emotional and behavioral problems, consequences of alcohol use, posttraumatic stress disorder features, conduct problems, tobacco use, externalizing behaviors, attention-deficit/hyperactivity disorder features, and cannabis use, but not eating-related problems, impaired functioning, internalizing behavior, or sleep-related problems. Psychoeducation had the highest effect size for ADHD features, affective symptoms, and interpersonal violence. Psychotherapy had the highest effect size for anxiety features. Conclusion Universal and selective preventive interventions for young individuals are feasible and can improve poor mental health outcomes.
更多
查看译文
关键词
intervention,meta-analysis,prevention,selective,universal
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