Traditional healing and medicine in dementia care for indigenous populations in North America, Australia, and New Zealand: A scoping review protocol
Bibliographic record
Abstract
Abstract Objective In this scoping review we aim to explore approaches to the integration of traditional healing and medicine in dementia care for Indigenous communities in North America, Australia, and New Zealand. We aim to understand the roles and experiences of traditional healers, strategies and their evaluation, and the barriers and opportunities for integration of Indigenous and western dementia care approaches. Introduction: Access to culturally safe geriatric care and dementia assessment, diagnosis, and care in Indigenous populations worldwide is an emerging challenge. The World Health Organization recognizes traditional healers as stakeholders in dementia care. Traditional healers can provide dementia assessment and care in unique ways and play a catalytic role in the process of dementia care planning. This scoping review will inform Indigenous health service providers about the potential integration of traditional healing and medicine into dementia care and support systems. Inclusion criteria: This review will include all types of study designs, that examine the integration of Indigenous approaches and traditional healers in dementia care in Indigenous communities in North America (Canada and U.S.), Australia and New Zealand. Methods In close collaboration with Indigenous community advisors, we will search for OVID MEDLINE, PsycINFO, CINAHL and EMBASE include English literature from 1990 to 2020. After two independent screens of title and abstract, and then full text, we will extract the study population, participants, intervention type, aims, methods, key findings, and knowledge gaps. The results will be summarized and presented to Indigenous community advisors for contextual interpretation and validation prior to being finalized. Results Our initial search of CINAHL, Embase, Medline and PsycINFO identified 516 records published between 2000 and 2020 that met our search criteria. After 164 duplicates were removed, we screened 352 titles and abstracts and excluded 209 that did not meet inclusion criteria. Our second stage review of 143 full text studies resulted in the further exclusion of 113 studies. Of these, 84 studies did not include traditional medicine and 13 did not involve Indigenous populations or meet our study design criteria. Results were extracted from the 30 eligible and relevant studies that were included in the final scoping review.
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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 | 0.109 | 0.081 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.023 | 0.018 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.047 | 0.010 |
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".