Exploring the value of mixed methods within the At Home/Chez Soi housing first project: a strategy to evaluate the implementation of a complex population health intervention for people with mental illness who have been homeless

Eric L Macnaughton, Paula N Goering, Geoffrey B Nelson
Canadian Journal of Public Health. Revue Canadienne de Santé Publique 2012 May 2, 103 (7 Suppl 1): eS57-63

OBJECTIVE: This paper is a methodological case study that describes the At Home/Chez Soi (Housing First) Initiative's mixed-methods strategy for implementation evaluation and discusses the value of these methods in evaluating the implementation of such complex population health interventions.

TARGET POPULATION: The Housing First (HF) model is being implemented in five cities: Vancouver, Winnipeg, Toronto, Montréal and Moncton.

INTERVENTION: At Home/Chez Soi is an intervention trial that aims to address the issue of homelessness in people with mental health issues. The HF model emphasizes choices, hopefulness and connecting people with resources that make a difference to their quality of life. A component of HF is supported housing, which provides a rent subsidy and rapid access to housing of choice in private apartments; a second component is support. Quantitative and qualitative methods were used to evaluate HF implementation.

OUTCOMES: The findings of this case study illustrate how the critical ingredients of complex interventions, such as HF, can be adapted to different contexts while implementation fidelity is maintained at a theoretical level. The findings also illustrate how the project's mixed methods approach helped to facilitate the adaptation process. Another value of this approach is that it identifies systemic and organizational factors (e.g., housing supply, discrimination, housing procurement strategy) that affect implementation of key elements of HF.

CONCLUSION: In general, the approach provides information about both whether and how key aspects of the intervention are implemented effectively across different settings. It thus provides implementation data that are rigorous, contextually relevant and practical.

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