A Way Home: Understanding the impact of human trafficking on Inuit women in Manitoba

ObjectivesA WAY HOME will work with the Inuit community to develop a culturally relevant and community-informed algorithm, which will determine prevalence rates of sexual exploitation and risk levels of exploitation for Inuit women in Manitoba, Canada. Overall, this project develops tools to support the health and well-being of Inuit. MethodsTo develop an Inuit-informed algorithm, this study uses a two-step process with a mixed method design. Step 1: the study will gather information on data fields and risk factors through input from an Inuit women’s sewing circle/focus group, using art-based methods to create traditional wall-hangings narrating the concept of risks/safety for Inuit women. Data fields will be identified through a thematic analysis of transcripts and wall-hangings. Step 2: Data fields will be used to populate a mixed systems estimation data analysis, using a pre-established cohort of Inuit women, and routinely collected data housed at the Manitoba Centre for Health Policy. ResultsWorking in partnership and collaboration with Inuit Elders and community, A WAY HOME will create an Inuit-formed algorithm that can help identify the number of Inuit women being sexually exploited and/or human trafficked, as well as a comprehensive risk identification fact-sheet, that can be used to identify the likelihood of risk and individual faces, as well as the approaches to decrease risk factors. Using Inuit Qaujimajatuqangit, Inuit traditional knowledge, this project will be a first attempt at combining Inuit knowledge and quantitative data analysis, successfully generating the first Inuit-informed algorithm on sexual exploitation prevalence rates and risk factors in Manitoba. ConclusionInuit and Inuit representatives have long advocated for meaningful inclusion in research and research objectives. This project will demonstrate how to work inclusively, in partnership and collaboration with Inuit using traditional Inuit knowledge and quantitative methods for prevalence estimation modelling.