Co‐Developing an Indigenous‐centred Model of Dementia Care in Alberta, Canada: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Current evidence favours a shift away from pharmacological to non-pharmacological approaches for the prevention and management of non-cognitive symptoms of dementia. With the limited effectiveness of pharmacological treatments and their risk of adverse effects, it is imperative to understand the effectiveness of non-pharmacological approaches to dementia care and lived experience. However, there has been little work to date to adapt non-pharmacological approaches in partnership with Indigenous people. The current model of dementia care is unsafe and inadequate and continued inaction will only lead to continued harm. The primary aim of this research was to develop an implementation-ready approach to Indigenous-centered dementia care that has been co-designed with partnering Indigenous communities. METHOD: This study prioritized ethical engagement with Indigenous community members. Participants were recruited through community social media and newsletters. Data were co-analysed using Indigenous approaches to thematic qualitative analysis to draft a preliminary structure for the Indigenous-centred dementia care approach. A modified Nominal Group Technique was held with a group of health care providers and decision makers that led to agreement on content and design, as well as the development of training materials for facilitators. RESULT: Four focus groups (five Indigenous participants with experience of dementia/group) were completed between October and December 2024 for participants to discuss potential content and modes of delivery of an Indigenous-centred dementia care intervention. Participants identified the need for: 1) culturally-specific brain health education that includes information on what dementia is; 2) Indigenous-specific risk identification and dementia prevention; 3) dementia-friendly Indigenous cultural and spiritual ceremonies; 4) programs for community and facility-living seniors that involved the traditions of local Indigenous Nations (e.g. jigging, beading, etc); and 5) in-depth training for staff to deliver the programming. CONCLUSION: This work will impact numerous populations that need culturally specific approaches to dementia care policy and planning by informing an ethical process to engaging with diverse populations. This work can help health and continuing care services provide community-specific approaches to non-pharmacological dementia 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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.035 | 0.012 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".