The Social Determinants of Health: Defining a Research Agenda for Canada’s Urban Aboriginal Population
Bibliographic record
Abstract
In the past few decades, the health of Aboriginal peoples has become a priority among Canadian health researchers (Adelson 2005; Reading and Nowgesic 2002). To date, however, the bulk of this literature has been overrepresented by research seeking to establish rates of disease and mortality, with considerably less attention paid to the social and economic processes underlying them. Indeed, while it is vital to characterize the health inequalities borne by Aboriginal Canadians—as they are vast, and, in some cases, growing—the fact remains that these observations have not transformed into a common understanding of the underlying causes of inequality, such as the important role played by the social determinants of health (SDOH) (Loppie and Wien 2009; Richmond and Ross 2009). What’s more, this body of research has tended to concentrate its efforts on segments of the Aboriginal population living on-reserve and in rural and remote areas, with appreciably less regard for patterns of urbanization that now characterize vast segments of the Aboriginal population. As of the last census count, 54% of the Aboriginal population lived in an urban centre, which includes large cities, or Census Metropolitan Areas (CMAs), and smaller urban centres. This was an increase from 50% in 1996 (Statistics Canada 2006). In spite of the recognition that increasing numbers of Aboriginal people now live in urban centres, and the challenges that this pattern of urbanization poses for Aboriginal health policy and program development, little substantive research has explored SDOH within the urban Aboriginal context. In this chapter, we examine if the current base of research on the social determinants of Aboriginal health reflects the population and geographic diversity of Canada’s increasingly urbanized population. We also describe what this base of literature looks like; specifically, we illustrate which SDOH have been examined, and through which methods this research is occurring. As an introduction, we begin with a discussion of the SDOH, and we demonstrate why the urban Aboriginal context is deserving of such research. We then describe the methodology we undertook in this review, followed by a discussion of our results. We conclude with a description of areas in which research is most urgently needed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.039 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.032 | 0.023 |
| Scholarly communication | 0.027 | 0.015 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.014 | 0.021 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".