Detailed Description: The investigators will conduct a phase II group-based, multi-component, and multi-level randomized behavioral clinical trial. The investigators will recruit and enroll 300 participants aged 15-20 (N=150 intervention and N=150 control) in Chicago, Illinois. After enrollment, participants will be randomized using a block-stratified randomization technique to ensure balance regarding race, ethnicity, and gender. Once participants are recruited, the investigators will use a computer-generated random number sequence to assign participants to the intervention group (Rise Community Engagement) or control group (Adulting 101: Life Skills Training). Participants assigned to the intervention arm will participate in 5 half-day, peer-based, interactive sessions teaching community engagement principles. Participants will be assigned to the control arm and will participate in 5 half-day interactive sessions teaching life skills training. Participants in the control arm will undergo life skills training with the same number of sessions and duration as the intervention arm.
Participants in both the intervention and control arms will report depressive symptoms on a clinically relevant measure (e.g., Patient Health Questionnaire-9) at baseline and then <1-, 6-, 12-, 18-, and 24- months post-initial 5-day community engagement or life skills training. In addition to depressive symptoms, the investigators will measure other aspects of psychological distress, including anxiety and stress, as secondary outcomes. Participants in both groups will have biometric samples, like blood draws, and clinical measurements of allostatic load at baseline and then 6-,12-, and 24- months post-initial 5-day community engagement or life skills training.
The proposed project will use a cluster randomized trial, which involves complete groups of individuals randomized to conditions (i.e., intervention, control), with clustering occurring in both arms. All of our statistical analyses will appropriately model the dependency among observations, which is a hallmark of multi-level modeling.