Context: Distributed medical education sites help train, recruit and retain doctors, notably in rural and isolated areas, by providing education and training in these areas and adapting their curriculum to meet the host community's health needs.
Objectives: The Centre de Formation Médicale du Nouveau Brunswick (CFMNB; New Brunswick Medical Education Centre) was established by a partnership between two academic institutions, the Université de Sherbrooke (University of Sherbrooke), situated in the province of Quebec, and the Université de Moncton (University of Moncton), situated in the province of New Brunswick, in Canada. The CFMNB is specifically targeting a minority community (Acadians). Working to establish a high-quality medical education programme, the CFMNB has also set community objectives to meet not only the health needs of this population, but also its social and economic needs.
Methods: This paper describes the overall objectives of this project, which are: to reduce the gap between community needs and academic institutional needs; to address ethno-cultural and language differences in a defined minority population, and to develop collaboration between the partners involved, including government and community entities which are often perceived as operating in isolation from one another. We also describe why and how the CFMNB developed community-focused objectives and the challenges that came with these innovations, and present lessons from the experience that may be relevant to other sites interested in the social responsibility of medical schools.
Conclusions: The CFMNB has produced interesting work and innovations in the field of social responsibility and has encountered many challenges. Continuing interaction between medical education, health research and health services to better address the needs of the population has been established. The information obtained by this process has been used to build a strategic plan for the CFMNB in order to ensure that it is socially responsive and has significant generalisable features.