Risk and protective factors of psychological pain using machine learning.

Psychological pain is of vital importance due to its link to suicidality. The present study sought to examine the psychometric properties of A Brief Measure of Unbearable Psychache, to investigate the roles of psychological distress, pessimism, meaning in life, hope, and demographic variables (age, gender, perceived economic status, marital status, and education level) in predicting psychological pain, to construct an optimal prediction model using a machine learning approach, to rank the feature importance of the possible predictors of psychological pain and to explain the contributions and direction of influence of the variables. This study included 432 participants (76.9% females), and the mean age of the participants was 24.26 years (SD = 6.47). The study findings determined that the psychometric properties of A Brief Measure of Unbearable Psychache had sufficient validity and reliability in Turkish culture. The results of the current study showed that the two most critical factors predicting psychological pain were psychological distress and pessimism, respectively. Hope and meaning in life were the other psychological variables. While psychological stress and pessimism had a positive effect, hope and meaning in life had a negative effect on psychological pain. The contribution of demographic factors was quite limited. These results highlight the relative superiority of psychological factors over demographic characteristics in predicting psychological pain. This study suggests the development of preventive and intervention programs aimed at reducing risk factors and strengthening protective factors for psychache sufferers or those at risk.