Considerations in Dementia Care for Indigenous Populations in Canada
Bibliographic record
Abstract
Introduction It is well known that population aging is occurring at a global scale. This trend is expected to continue in coming decades, but not all demographic cohorts are affected in the same way. Over the past two decades, age-related dementias have surfaced as a growing health concern in Indigenous populations globally (Henderson, 2002, 2009; Smith et al, 2008; Jacklin et al, 2013a, 2015a; Radford et al, 2015), yet data on incidence and prevalence remain limited (Warren et al, 2015). Recent research suggests the increase of dementia in Indigenous communities may be attributable to a combination of complex causes, such as changing perceptions of the illness, demographic transitions, impacts resulting from the social determinants of health, exposure to risk, increased vulnerability, and co-morbidities (Jacklin et al, 2013a). The emergence of dementia as a health disparity in Indigenous populations is set among a backdrop of health inequities experienced by Indigenous populations worldwide (Gracey and King, 2009; Sequist, 2017). Increasingly in recent years, scholars are acknowledging the need to explicitly address the underlying structures within health systems and policies that perpetuate health inequities for Indigenous and ethnic minority populations by incorporating frameworks such as structural violence, social justice, and health equity into research strategies (Farmer et al, 2006; Dilworth-Anderson et al, 2012; Bailey et al, 2017; Stanley et al, 2017). Along with this, there is growing acknowledgement that a careful consideration of Indigenous culture and context is necessary to develop appropriate frameworks for the delivery of equitable healthcare strategies (Browne et al, 2017; Jacklin et al, 2017b; Crowshoe et al, 2019). This chapter provides an overview of what is known about the experience of dementia in Indigenous populations in North America and examines this knowledge using a cultural safety and health equity lens for rural and remote Indigenous communities in Canada. We construct a health equity argument by first establishing the dementia disparity in Indigenous peoples within the context of complex social determinants of health unique to this population. We then provide a brief overview of how both cultural and systemic forces affect the dementia experience and access to care. Cultural safety is used as a framework to understand how to respond and how to create more equitable dementia outcomes.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".