Identifying well-being profiles and resilience characteristics in ex-members of fundamentalist Christian faith communities.

There is a lack of empirical research on the heterogeneity in well-being of individuals who disaffiliated (i.e., left or were expelled) from an exclusionary and demanding faith community. Thus, little quantitative knowledge exists on factors related to resilience in these individuals. Therefore, the study aims were twofold: (1) to identify profiles of well-being in ex-members; and (2) to examine the characteristics of the identified profiles. A cross-sectional online survey assessed ex-members of various fundamentalist Christian faith communities. Latent profile analysis identified latent heterogeneity within the sample. Well-being profile indicators included perceived stress, psychopathological symptoms, affect, and satisfaction with life. Profile-related characteristics included socio-demographics (i.e., gender, age), membership (i.e., reason for joining, duration, extent of involvement, reasons for exit, social support during exit, and time since the exit), and resilience-supporting resources (i.e., social support, self-esteem, sense of coherence, personality, socio-economic status). In the final sample (N = 622, Mage = 41.34 years; 65.60% female), four distinct profiles were identified: resilient (25.70%), normative (36.40%), vulnerable (27.20%), and adverse (10.70%). The resilient profile was characterised by higher age, lower reporting of abuse or maltreatment as exit reason, and highest levels of resilience-supporting resources. Ex-members of fundamentalist Christian faith communities differ substantially in their well-being. Membership aspects were only weakly related to current well-being, with the exception of the exit reason of abuse or maltreatment. This study provided novel quantitative insights into the well-being profiles of individuals who disaffiliated from a fundamentalist Christian faith community in German-speaking countries.

Uncovering social-contextual and individual mental health factors associated with violence via computational inference.

The identification of human violence determinants has sparked multiple questions from different academic fields. Innovative methodological assessments of the weight and interaction of multiple determinants are still required. Here, we examine multiple features potentially associated with confessed acts of violence in ex-members of illegal armed groups in Colombia (N = 26,349) through deep learning and feature-derived machine learning. We assessed 162 social-contextual and individual mental health potential predictors of historical data regarding consequentialist, appetitive, retaliative, and reactive domains of violence. Deep learning yields high accuracy using the full set of determinants. Progressive feature elimination revealed that contextual factors were more important than individual factors. Combined social network adversities, membership identification, and normalization of violence were among the more accurate social-contextual factors. To a lesser extent the best individual factors were personality traits (borderline, paranoid, and antisocial) and psychiatric symptoms. The results provide a population-based computational classification regarding historical assessments of violence in vulnerable populations.