ResiMETA Meta-Analyses

ResiMETA is an open-access, continuously updated database for trajectory-based resilience research summarizing over 300 studies on mental health responses to stress and the predictive value of psychosocial resilience factors. It is conducted by the Leibniz Institute for Resilience Research in Mainz, Germany.

This study systematically synthesizes evidence on mental health trajectories after natural disasters using the ResiMETA database to identify common patterns of mental distress and well-being, their prevalence, determinants, and the predictive role of psychosocial resilience factors.

Building on the open-access database ResiMETA, this systematic review and meta-analysis aims to address key research gaps in resilience trajectories and resilience factors among children, adolescents, and young adults. Specifically, we seek to identify the most common trajectories of mental distress and/or positive mental health following macro stressor exposure in this population and to estimate the prevalences of different trajectories across various mental health outcomes. Moreover, we examine ...

The current project aims to conduct the first systematic review summarizing knowledge on resilient responses as well as on resilience factors that shape those responses to stress among populations in SSA. Given the global relevance of resilience research, there is a critical need to examine the applicability of key findings in African populations, and to study specific nuances in African contexts.

It is our aim to close this gap with systematically synthesizing evidence on multilevel (i.e., individual, social, organizational, societal) resilience factors and resilience mechanisms in working populations with a special focus on the subgroup of HCWs.

This project aims to address the outlined research gaps by synthesizing evidence from longitudinal studies on mental health trajectories and resilience factors in cancer survivors with focus on: (1) identifying common trajectories of mental distress and/or positive mental health, (2) estimating the prevalence of these trajectories and (3) examining the predictive value of resilience factors for trajectory probabilities. This review will be conducted in collaboration with the ResiMETA database (An ...