Traditional healing and medicine in dementia care for Indigenous populations in North America, Australia, and New Zealand: Exploring culturally‐safe dementia care policy from a global perspective
Bibliographic record
Abstract
INTRODUCTION: In 2018, the World Health Organization recognized traditional healers as community stakeholders in dementia care. This scoping review aimed to summarize the existing dementia care literature regarding strategies for the integration of traditional healing in dementia care and the roles of traditional healers. METHODS: A group of Indigenous Elders from Northern Ontario, Canada, guided, reviewed, and validated the research process and findings. The Joanna Briggs Institute approach was applied to a structured search strategy across the CINAHL, Embase, MEDLINE, and PsycINFO databases. A title and abstract screening were completed, followed by a full-text assessment of the identified manuscripts. RESULTS: A total of 143 full manuscripts were reviewed, of which two studies fully met the community-determined inclusion/exclusion criteria. DISCUSSION: The integration of traditional healing practices into dementia care offers a pathway to culturally-safe care for people with dementia. The findings identified policy advocacy as key to engage, educate, and empower traditional healers. Highlights: The WHO recognized traditional healers as community stakeholders in dementia care and prevention worldwide in 2018; however, traditional healers are underrepresented and marginalized in healthcare systems due to the lack of culturally-safe dementia care (CSDC) policies at community and national levels globally.Community-based CSDC models were critically reviewed and validated by local Indigenous community stakeholder consultations.The result is a call to action to assist the WHO and Alzheimer's Disease International in developing guidelines for CSDC policy improvements with the global Indigenous community for the engagement and empowerment of traditional healers to navigate dementia care and to implement the WHO Global Action Plan on the Public Health Response to Dementia (2017-2025).Integration of Western biomedical and Indigenous traditional healing and medicine in dementia care in the healthcare system can reduce health disparities and empower traditional healers on a global scale. Indigenous-led models that include traditional healers in dementia care are critical for improving equity gaps in dementia care for Indigenous Peoples.
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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 | 0.027 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".