Data analysis
Descriptive analysis was conducted on each of the study variables (i.e., three sociodemographic variables, three dimensions of civic engagement, and the two mental health outcomes) to ascertain their pattern of distribution as well as the bivariate associations between the variables. Intervention condition was excluded from the analysis after the preliminary analysis did not reveal any statistically significant associations with the study variables. Longitudinal measurement invariance (i.e., configural invariance, metric invariance, and scalar invariance) was then estimated to ensure that meaningful comparisons can be made across the three time points (i.e., T1, T2, and T3). Configural invariance is when the items load onto the latent factor (each of the civic engagement and mental health dimensions) in the same manner across time. For metric invariance, the factor loadings of the items measuring civic engagement and mental health are the same across time points. For scalar invariance, the factor loadings, as well as the intercepts, are the same across time.Following the longitudinal measurement invariance, latent class growth analysis (LCGA) was used to estimate trajectories of mental health (mental distress and mental well-being) where different trajectories were identified from the data. In LCGA, the variance and covariance estimates of the growth factors are fixed to 0, a statistical procedure that does not allow for variation within classes (The LCGA analyses were examined for non-convergence and local maxima and treated accordingly by increasing the number of random starting values. Except for the descriptive analysis, which was conducted using the Statistical Product and Service Solutions (SPSS statistical program), all other analyses were run using the Mplus statistical program, version 8 (