Integrating conventional science and aboriginal perspectives on diabetes using fuzzy cognitive maps
Introduction
The health of Aboriginal communities in Canada is generally lower than that of comparable non-Aboriginal communities. In particular, non-insulin-dependent diabetes mellitus (Type 2) (NIDDM) rates are significantly higher among Aboriginals compared to the general Canadian population (Bruce, 2000; Young, Reading, Elias, & O’Neil, 2000;) especially for Aboriginal women (Green, Blanchard, Young, & Griffith, 2003; Kelly & Booth, 2004) and children (Dean, 1998; Harris, Perkins, & Whalen-Brough, 1996). Diabetes prevalence in Aboriginal communities continues to increase, despite being a priority concern for health agencies (Health Canada, 2000).
Diabetes is seen as a new disease within Aboriginal communities, predominately linked to the move away from traditional hunter-gather to a more western lifestyle (Gracey (1995), Gracey (2000); O’Dea, 1991; Zimmet, 1979; Zimmet, McCarty, & de Courten, 1997). The conventional scientific view is that adoption of a western lifestyle has led to decreased physical activity, increased prevalence of obesity, and major shifts in diet. These factors, potentially overlaying a genetic susceptibility, have resulted in the observed increases in diabetes incidence (Ben-Haroush, Yogev, & Hod, 2004; Daniel, Rowley, McDermott, & O’Dea, 2002; McDermott, O’Dea, Rowley, Knight, & Burgess, 1998; Rowley & O’Dea, 2001; Young et al., 2000).
For many Aboriginals, however, conventional science fails to recognize the true root causes of diabetes: a life out of balance due to the loss of traditional lifestyles, community, culture and spirituality (Garro, 1995; Iwasaki, Bartlett, & O’Neil, 2005; London & Guthridge, 1998; Thompson & Gifford, 2000). There is, furthermore, evidence that within Aboriginal communities there is a belief that the determinants of diabetes are beyond individual control (Gregory et al., 1999; Potvin, Cargo, McComber, Delormier, & Macaulay, 2003); this contrasts sharply with the conventional view that emphasizes individual lifestyle choices such as diet and physical activity.
These perceived differences between Aboriginal and conventional scientific perspectives on health reduce the likelihood of success of diabetes prevention programs based solely on the science perspective. The perceived lack of control over diabetes, for example, has been identified as a barrier to treatment and prevention aimed at individual lifestyle changes (Bisset, Cargo, Delormier, Macaulay, & Potvin, 2004; Daniel et al., 1999; Daniel & Messer, 2002; Garro, 1995; Gregory et al., 1999).
The integration of traditional Aboriginal and conventional scientific knowledge (SK) in diabetes management faces several hurdles. Particularly problematic is the way in which knowledge is stored and transmitted. Traditional knowledge (TK) is generally seen as being held collectively in a largely qualitative form (Gervais, 2003). In comparison, conventional SK is predominately based on quantitative evidence, and often restricted to a small group of experts. The difficulties associated with measuring or manipulating the socio-cultural and spiritual determinants of diabetes also limits their contribution to conventional science-based treatment and prevention programs (Wilson & Rosenberg, 2002).
The Mohawk Council of Akwesasne (MCA) and the Institute of the Environment, University of Ottawa have undertaken a project to incorporate the local Aboriginal perspective of diabetes into health policy. The first step was to articulate the views held by the Akwesasne community and compare them to those held by members of the conventional scientific community. In what follows, we refer to the former as TK, the latter conventional SK, even though it is clear that TK may include strongly scientific elements (i.e., systematic observation, hypothesis formulation and prediction). Here fuzzy cognitive mapping is used to represent and compare TK and SK with respect to the causal determinants of diabetes and demonstrate how FCM may be used to explore the impacts of different management scenarios, given the articulated causal relationships in a given knowledge domain.
Section snippets
FCM
Fuzzy cognitive maps (FCM) are graphical representations of the relationships between elements of a system (or issue), as perceived by “experts”, where an expert is any person with knowledge of the system under scrutiny. FCM comprise vertices, representing concepts (C), joined by directional edges (connections) representing causal relationships between concepts. Each connection is assigned a weight aij∈[−1,1] which quantifies the strength of the causal relationship between concepts Ci and Cj (
The SK FCM
Our literature search yielded 40 studies, of which 24 had sufficient data to estimate effect sizes. The resulting collective SK FCM contained 31 concepts and 50 connections (Fig. 2a). Visual inspection of the FCM suggested the complexity was inflated by the large number of individual diet elements (e.g. total calories, total carbohydrates, etc.). This level of resolution was inappropriate for comparison with the TK FCM, hence these elements were eliminated by converting them to effect sizes
Exploring the management implications of different FCMs
Simulation of an FCM may be used to explore how the system described will respond given a particular set of inputs. Simulation is an iterative process in which a state vector (In) representing the activation level of each concept is multiplied by the adjacency matrix (A) to generate a new vector (In+1). The resulting value for each concept (Ij) in the new vector is the sum of inputs from all concepts (i) in the FCM calculated as; The outcome vector
Discussion
This study serves as a first attempt to systematically record an Aboriginal view of diabetes. Neither the TK nor SK diabetes FCMs presented here are complete. The TK FCM was constructed by 3 health professionals from a single community: there is no reason to believe that this representation would be consistent among different constituencies within or among communities. Indeed, it is plausible that the set of risk factors presented may be community-specific, based on environmental context and
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