ABSTRACT
Displacement is detrimental not only to displaced individuals and families but also to the communities left behind and their ability to collectively resist and mobilize against global processes that negatively affect their ability to engage in practices of resilience and regeneration that support well-rooted communities. Critical approaches to the study of displacement should not only focus on mapping vulnerability factors and analyzing dominant power structures driving racial, social, and environmental injustice but should also include the collective resilience, everyday vitality, and community knowledge that characterize rooted urban neighborhoods and build immunity to serial forced displacement. Building on theoretical and methodological foundations in critical, Black, Latinx, and Indigenous geographies;Black feminist theory;and environmental justice, we argue that for mapping to have a positive change outside the already academic understanding of displacement and inequity, we need a methodology to (1) identify intersectional oppressions and name them as such, (2) center community knowledge and strengths enabling resilience, and (3) advance community activism. This methodology requires trust and community engagement but is vulnerable to systems that interrupt the embeddedness of researchers. The response to the COVID-19 pandemic is one such system;not only has the pandemic exacerbated displacement crises, making the need for engaged, critical, and cocreative partnerships even greater, it has abruptly halted opportunities for these community partnerships and regenerative work to happen. Drawing on our experiences attempting these approaches during the COVID-19 pandemic, we discuss challenges that arise when researchers are displaced from field sites, best practices, and implications for future research.
ABSTRACT
During the first wave of the COVID-19 pandemic it emerged that the nature and magnitude of demand for mental health services was changing. Considerable increases were expected to follow initial lulls as treatment was sought for new and existing conditions following relaxation of 'lockdown' measures. For this to be managed by the various services that constitute a mental health system, it would be necessary to complement such projections with assessments of capacity, in order to understand the propagation of demand and the value of any consequent mitigations. This paper provides an account of exploratory modelling undertaken within a major UK healthcare system during the first wave of the pandemic, when actionable insights were in short supply and decisions were made under much uncertainty. In understanding the impact on post-lockdown operational performance, the objective was to evaluate the efficacy of two considered interventions against a baseline 'do nothing' scenario. In doing so, a versatile and purpose-built discrete time simulation model was developed, calibrated and used by a multi-disciplinary project working group. The solution, representing a multi-node, multi-server queueing network with reneging, is implemented in open-source software and is freely and publicly available.