What is Needed to Develop a Culturally Congruent Model of Indigenous Dementia Care in Alberta, Canada? A Qualitative Study
Bibliographic record
Abstract
Background: Approximately 10,800 Indigenous people in Canada were living with dementia in 2020 (Alzheimer Society of Canada, 2024). It is projected that by 2050, these numbers will rise to 40,300—an increase of 273%, while the projected increase for the overall population is 187% (Alzheimer Society of Canada, 2024). There is work in progress to create culturally sensitive screening and assessment tools to evaluate cognition for Indigenous individuals living with dementia (Walker et al., 2021a). However, without simultaneous development of culturally congruent services, Indigenous people living with dementia will continue to suffer harm and inadequate service from current care models. Current dementia care often perpetuates the legacies of colonization, highlighting the need for a culturally congruent approach that integrates Indigenous-led components and is responsive to the lived experiences of Indigenous people. Aims: To understand what is needed to co-develop a freely available Indigenous-centred dementia model of care in partnership with Indigenous individuals living with dementia, their families, and communities. Methods: To prioritize ethical and decolonial research approaches, sequential focus groups (Jacklin et al., 2016) were used alongside Keeoukaywin (The Visiting Way) (Gaudet, 2018)-a Métis theoretical way of knowing- guided by a group of advisors and Elders. Sequential focus group guides included questions about content and delivery methods of a potential intervention informed by an understanding of Indigenous care, as derived from current literature and an Indigenous framework created from the VOICES study. The data were co-analyzed using Indigenous approaches to thematic analysis (Drawson et al., 2017; Gaudet, 2018). Findings: An Indigenous-centred dementia care model requires an adaptable approach that reflects the realities of each person’s context and cultural values. Three themes were developed: Indigenization, Personalization, and the Intersection of Challenges and Innovations, to illustrate how dementia care can be operationalized. Conclusion: This research offers a foundation for developing dementia care that truly aligns with Indigenous ways of doing. This research will contribute to the development of resources, including care manuals centred on Indigenous knowledge, that will strengthen the capabilities of healthcare professionals and better equip Indigenous communities to have autonomy over their care.
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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 | 0.017 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.042 | 0.017 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".