Methods. Users’ posts were independently coded by one coder (N = 39). Descriptive Coding and Affect Coding were used as the first cycle coding methods. Descriptive was used to explore links within the data. Affect coding was used to explore emotions and/or feelings participants discussed. These processes resulted in 109 codes in total. Focused Coding was used as the second cycle coding method to categorize the data by identifying the most significant or frequent codes within the data corpus (Saldana, 2021).
Results. Following themes emerged: Psychopathology, Student Loans, Interference with Life, Interpersonal Relationships and Resilience. Psychopathology referred to various symptoms (e.g., psychosis), diagnoses (e.g., schizophrenia, depression), comorbidities (substance use), suicidal behavior, and experiences stigma and/or shame related to their mental health. Student Loans were characterized by how individuals felt stressed and overwhelmed about their debt, experiences with student loan forgiveness, and viewpoints about the specific benefits of living outside of the United States. Interference with Life captured individuals’ experiences with symptoms of psychopathology and/or student loans negatively affecting their life goals and trajectories in addition to a sense of job insecurity. Interpersonal Relationships referred to increased conflicts with family/friends, as well as support that was received by family/friends. Lastly, Resilience was characterized by various advice (e.g., emphasizing self-care, budgeting tips), normalizing and empowering individuals who have student loan debt, positive statements about student loans, and encouraging the usage of medications.
Conclusions. Given the significant public health concerns regarding financial instability and poor mental health outcomes, the current study contributes to the literature by highlighting various resilience factors that may be present within these vulnerable populations. Further, specific areas to examine, such as interpersonal relationships and life trajectories, are highlighted. These findings can be used to inform future research and intervention efforts that could be beneficial for these at-risk populations struggling with student loan debt.