Examining worry and secondary stressors on grief severity using machine learning.

Background & objectives: Worry and loss-related secondary stressors appear to be important correlates of problematic grief responses. However, the relative importance of these variables in the context of established correlates of grief responding, ranging from indicators of identity disruption and demographic characteristics of the bereaved to characteristics of the loss of quality of the relationship with the deceased, is unknown. Modeling the relative associations of these factors can be problematic, given the high degree of collinearity between these variables. This study used a machine learning approach to provide accurate estimations of the relative importance of these correlates for post-loss symptom severity.

Methods and results: A convenience sample of 428 bereaved people who had lost a parent, spouse, or child in the last 30 to 365 days completed an online survey. Random forest regression modeling examined the effects of worry and secondary stressors on symptom severity in the context of established correlates. Results indicated worry and the number of secondary stressors experienced were among the factors most strongly associated with severity of grief, depression, posttraumatic stress and problems functioning.

Conclusions: These results also provide insight into the relative importance of worry and secondary stressors affecting grief severity to guide future research.