Abstract
BACKGROUND: Despite decades of advancement to support interventions for managing work-related stress, mental health issues have significantly escalated among healthcare professionals. Effort-reward imbalance (ERI) and overcommitment in the workplace are linked to several psychiatric disorders. However, the underlying biological mechanisms remain unclear. This study investigated whether ERI and overcommitment among healthcare professionals were linked to Allostatic Load (AL) and whether AL mediates the relationship between ERI, overcommitment and mental health issues. METHODS: One hundred forty-two nursing workers (n = 142; 90.1 % female, mean age: 39.5 ± 9.6) were randomly recruited from a university hospital in Sao Paulo, Brazil, and applied the ERI scale that assesses work effort, reward, and overcommitment. The Perceived Stress Scale (PSS), The Beck Depression Inventory (BDI), and the Self-Report Questionnaire for psychiatric symptoms (SRQ-20) evaluated the mental health outcomes. Ten neuroendocrine, metabolic, immunologic and cardiovascular biomarkers were analyzed, and values were transformed into an AL index using clinical reference cutoffs. RESULTS: Linear regression adjusted for covariates showed that higher scores for overcommitment were associated with higher AL indexes, which in turn were associated with higher SRQ-20, but not with PSS and DBI scores. As expected, higher scores for effort, lower for reward, and higher ERI were associated with higher scores for PSS, SRQ-20, and DBI, but not with AL index. Direct effect estimates showed that overcommitment was directly associated with higher SRQ-20 scores, and indirectly via AL. CONCLUSION: Our study reveals that overcommitment, rather than ERI, was linked to increased AL in healthcare workers. Additionally, AL mediates the relationship between overcommitment and higher psychiatric symptoms, highlighting a key mechanism by which work stress can lead to mental health problems. Individual's responses to high work demands need to be considered when designing predictive models and interventions for mental health issues.