Correction Workers’ Burnout and Outcomes: A Bayesian Network Approach

The present study seeks to demonstrate how Bayesian Network analysis can be used to support Total Worker Health(®) research on correction workers by (1) revealing the most probable scenario of how psychosocial and behavioral outcome variables in corrections work are interrelated and (2) identifying the key contributing factors of this interdependency relationship within the unique occupational context of corrections work. The data from 353 correction workers from a state department of corrections in the United States were utilized. A Bayesian Network analysis approach was used to probabilistically sort out potential interrelations among various psychosocial and behavioral variables. The identified model revealed that work-related exhaustion may serve as a primary driver of occupational stress and impaired workability, and also that exhaustion limits the ability of correction workers to get regular physical exercise, while their interrelations with depressed mood, a lack of work engagement, and poor work-family balance were also noted. The results suggest the importance of joint consideration of psychosocial and behavioral factors when investigating variables that may impact health and wellbeing of correction workers. Also, they supported the value of adopting the Total Worker Health(®) framework, a holistic strategy to integrate prevention of work-related injury and illness and the facilitation of worker well-being, when considering integrated health protection and promotion interventions for workers in high-risk occupations.