Depression is one of the most common and debilitating health problems, however, its heterogeneity makes a diagnosis challenging. Thus far the restriction of depression variables explored within groups, the lack of comparability between groups, and the heterogeneity of depression as a concept limit a meaningful interpretation, especially in terms of predictability. Research established students in late adolescence to be particularly vulnerable, especially those with a natural science or musical study main subject. This study used a predictive design, observing the change in variables between groups as well as predicting which combinations of variables would likely determine depression prevalence. 102 under- and postgraduate students from various higher education institutions participated in an online survey. Students were allocated into three groups according to their main study subject and type of institution: natural science students, music college students and a mix of music and natural science students at university with comparable levels of musical training and professional musical identity. Natural science students showed significantly higher levels of anxiety prevalence and pain catastrophizing prevalence, while music college students showed significantly higher depression prevalence compared to the other groups. A hierarchical regression and a tree analysis found that depression for all groups was best predicted with a combination of variables: high anxiety prevalence and low burnout of students with academic staff. The use of a larger pool of depression variables and the comparison of at-risk groups provide insight into how these groups experience depression and thus allow initial steps towards personalized support structures.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)