Summary: | Increasing and unprecedented climate change and variability is influencing population health and wellbeing via a number of interacting and interconnected pathways throughout the Circumpolar North. Integrated environment and health surveillance systems can provide important tools for Northern communities, governments, and public health professionals to understand, detect, attribute, and adapt to impacts of climate change. However, existing integrated surveillance systems are often not designed to consider or address Inuit-specific conceptualizations of wellbeing or climate change. The research shared in this dissertation grew out of a larger, community-driven project initiated in 2015 by the Inuit community of Rigolet, Nunatsiavut, Canada, that sought to design, pilot, and evaluate the eNuk program: an Inuit-led integrated surveillance system. Emergent from community needs and priorities, the central goal of this dissertation was to work with Rigolet Inuit to characterize the contributions of Inuit knowledge, values, perspectives, and lived experiences in influencing and enhancing processes involved in monitoring and responding to impacts of climatic and environmental changes on Inuit wellbeing in the Circumpolar North. Through a collaborative, multi-year, qualitative case study conducted in partnership with Rigolet Inuit, data were drawn from 41 in-depth, semi-structured interviews with Inuit community and Inuit government partners in Nunatsiavut between August 2015 and October 2018. Decolonizing, community-led methodologies informed thematic analyses of these data, which were guided by grounded theory approaches. Inuit voices shared within this dissertation offer a means of reconceptualizing what integrated surveillance can and should look like if it is to be Inuit-led and Inuit-focused, and what kinds of information it can provide for place-based climate change adaptation. Findings show how Inuit self-determination over the generation, interpretation, and use of integrated surveillance information can ...
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