Various studies have indicated that unaccompanied minor refugees (UMRs) have a higher risk of suffering from mental health problems than do accompanied minor refugees and general population norm. However, only a few studies provide data on UMRs regarding post-migration risk factors, their interrelations, and their influence on mental health. In this study, system models of post-migration risk factors for mental health and their interactions were developed in the case of Austria. In three consecutive interactive workshops with scientists and practitioners, fuzzy-logic cognitive mapping techniques were used to integrate the experts’ knowledge. The resulting final system model consists of 11 risk factors (e.g., social contacts in the host country, housing situation, or professional health care services). The model provides a deeper insight into the complexity of interrelated direct, indirect, and reciprocal relations, as well as self-reinforcing triads. This systemic approach provides a sound basis for further investigations, taking into account the inherent complex multifactorial dependencies in this topic.

More and more, the evaluation of complex projects is being related to the capacity of the project to deal with crucial social, economic, and environmental issues that society is responsible for and with the activation of systemic changes. Within this “mission” perspective, growing attention is given to learning in action. This paper aims at (i) conceptualizing a methodological framework for complex project evaluation within the context of the Triple-Loop Learning mechanism and (ii) showing its application in a European project as well as including the toolbox developed in coherence with the elaborated framework. It does so by looking at the case of an ongoing Horizon 2020 project aiming to develop language-oriented technologies supporting the inclusion of migrants in Europe. In particular, the paper looks at Triple-Loop Learning as pushed by the reflection on three dimensions: the “what”, the “how”, and the “why” of collective actions in complex projects. The consequent learning process is expected to have transformational potentials at the individual, institutional/organizational, and (in the long term) up to the societal scale. By exploring the opportunities offered by the evaluation tools in the easyRights project, the study highlights the potential of nurturing a wider, arguably neglected as yet, learning space for understanding, engaging, and transforming real contexts and thus developing more effective contribution to the needed transition.

The lack of access to basic services played a big part among the key effects of COVID-19 on migrants and refugees. This paper examines the governance dynamics behind public services for migrants and refugees to understand how COVID-19 has impacted them and what accounts for different levels of adaptive capacity. It employs a mixed methods approach, using egocentric network analysis and qualitative interviews to compare the service ecosystems in four European cities from 2020 to 2022 (Birmingham, Larissa, Malaga, and Palermo). The paper explores the impact of two conditions on the service ecosystems’ ability to adapt to the pandemic: the structure of governance and the presence of dynamic capabilities. We argue that the ability of local governments to manage pandemic challenges is highly dependent on the formal distribution of comprehensive competences across various levels (the structure of governance), and the quality of network cooperation between different administrations and civil society (dynamic capabilities). Our analysis reveals that while both conditions are critical for the level of adaptive capacity in public services’ provision, the structure of governance is more likely to act as a constraint or trigger for coping strategies.

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