Continuous Versus Grouping Approaches to Resilience: Predicting Mental Health Following Adversity in Two Longitudinal Datasets.

Objective: Resilience in psychiatry is often defined as the absence of psychopathology following early adversity. Resilience may be better operationalized as a continuous construct reflecting the degree to which individuals are functioning better than would be predicted given their adversity exposure. We used a continuous metric of resilience to predict mental health problems in 2 richly phenotyped samples studied longitudinally, and compared findings to those of conventional grouping approaches. Method: Exposure to early adversity was assessed at baseline (Study I: N = 1,265; Study II: N = 191), and mental health was assessed several times over a decade (9-21 years of age). Resilience was defined as the residual of participants' mental health symptoms accounting for early adversity. We then used generalized additive models to examine mental health symptoms across adolescence as a function of resilience to adversity. Finally, we compared continuous approaches with grouping approaches to assess resilience. Results: In both studies, greater resilience to adversity in childhood predicted fewer mental health problems in adolescence. Although both approaches to modeling resilience predicted unique variance in subsequent mental health symptoms when modeled simultaneously, the grouping approaches identified fewer individuals as resilient than did the continuous approach. Conclusion: A continuous approach to operationalizing resilience significantly predicted mental health symptoms across adolescence. Furthermore, both continuous and categorical operationalizations predicted unique variance in mental health outcomes. Importantly, however, grouping approaches may overlook a significant number of individuals who are, by a continuous assessment, resilient. Determining the optimal approach for identifying resilient individuals should be a priority for mental health research.