Objective(s): Depression is one of the most common and debilitating mental disorders. Identifying predictors of depression is crucial for the prevention and treatment of this disorder. The variety and complexity of these predictors, along with the potential for multicollinearity, make conventional statistical analysis methods less effective in examining the relationships between these variables and depression. This study aimed to identify the predictors of depression among community Health Workers (RHWs) using the Ridge-PLS method.
Methods: This secondary study investigated a sample of 394 RHWs in East Azerbaijan province. Data were collected using a questionnaire designed to assess predictors of depression in RHWs based on the social-ecological model, comprising 24 questions on a 5-point Likert scale. Depression was diagnosed using the Beck Depression Inventory. The Ridge-PLS statistical method was utilized for data analysis.
Results: Among the 394 RHWs screened, 170 (43.2%) exhibited mild to severe depressive symptoms. According to the Ridge-PLS method, the factors associated with depression can be categorized into two general areas: personal factors and environmental factors.
Conclusion: The findings of this study suggest that the 24 items can be condensed into two dimensions: personal and environmental. Within the personal dimension, self-care can help mitigate the incidence of depression, while improvements in work and social conditions can further reduce depression rates in society.