Resilience to major life events: Advancing trajectory modeling and resilience factor identification by controlling...

Resilience has been defined as the maintenance or quick recovery of mental health during and after stressor exposure. One popular operationalization of this concept is to model prototypical trajectories of mental health in response to an adverse event, where trajectories of undisturbed low or rapidly recovering symptoms both comply with the resilience definition. However, mental health responses are likely also influenced by other stressors occurring before or during the observation time window. These "background" stressors may affect a person's assignment to a trajectory class. When using these classes as dependent variables to identify resilience-predictive factors, this may lead to false estimates. A new method to build exposure-controlled trajectories based on time courses of stressor reactivity (SR), rather than pure mental health scores, is demonstrated on a data set of 707 initially healthy participants living in Germany (67.33% female; Mage = 29.20, SD = 8.27). SR scores express individual deviations from the sample's normative mental health reaction to observed real-life stressors during the observation time window, thus accounting for individual differences in exposure to background stressors. The resulting trajectory models are plausible. In analyses additionally controlling for background stressors occurring before the observation time window (past life events), low SR trajectories are predicted by the well-documented resilience factor sense of coherence, suggesting construct validity. Further, they are associated with lower odds of developing categorical mental health conditions, suggesting predictive validity. Our study provides the first proof of principle for a refined method to identify predictors of resilience to major stressor events. (PsycInfo Database Record (c) 2024 APA, all rights reserved).