Dementia Risk Reduction Education Programs and Resources for Indigenous Peoples of Canada, Aotearoa New Zealand, United States of America and Australia: A Scoping Review
Bibliographic record
Abstract
Educational health promotion programs and resources support people to make informed decisions and change their behaviours. Dementia, a name for a group of degenerative brain diseases, affects over 55 million people across the globe. Currently, dementia risk reduction (DRR) is a global health priority as dementia has no known cure. Consequently, educational programs and resources that focus on DRR respond to the global health priority by targeting potentially modifiable risk factors. A project currently being undertaken by the research team is focussed on supporting DRR in Aboriginal and Torres Strait Islander Peoples' primary care settings in Queensland, Australia. One strategy adopted by the research team is to identify safe and appropriate DRR programs and resources that could be integrated into primary care settings. Consequently, the aim of this scoping review was to identify and determine the quality of DRR programs or resources that have been developed or used with Indigenous peoples of Canada, Aotearoa New Zealand, the United States of America, and Australia. The Joanna Briggs method for scoping reviews was used to identify programs and resources developed with, for and by Indigenous peoples of the target countries. Appropriate databases including CINAHL and Medline as well as Google searches for grey literature published in English since 2010 were used to identify sources. Eleven sources were identified. One source was a published article, the other ten resources were videos (n = 5), websites (n = 2) and electronic written resources (n = 3). Given the paucity of evidence of DRR programs and resources currently available for Indigenous peoples the following recommendations are made for future development. They need to: (1). Be firmly grounded in Indigenous health promotion principles and theoretical frameworks and co-designed with, by and for Indigenous peoples. (2). Provide information about how dementia risk can be reduced; and (3). Linked with chronic disease interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.016 | 0.023 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".