What can we learn about dementia from research in Indigenous populations?
Bibliographic record
Abstract
Indigenous peoples represent up to 5% of the world's population (almost 400 million people), representing thousands of individual cultures and language groups. The health status of older Indigenous peoples has been little researched, partly related to lower life expectancy and the consideration that Indigenous peoples do not live long enough to experience the common “geriatric syndromes” such as dementia, frailty, and falls. Statistics from Australia and Canada now report that Indigenous populations are undergoing rapid aging, with many examples of survivorship to old age (Arkles et al., 2010; Jacklin et al., 2012). The systematic review by Warren et al. (2015) is a timely one, in that it reminds clinicians interested in old age that this “fourth” World population deserves further attention. Researchers that have worked with these groups to produce population estimates are relatively few. In their systematic review, Warren et al. (2015) demonstrate wide variation in prevalence rates of dementia. They conclude that a major cause of this heterogeneity in prevalence is due to basic methodological differences. In particular, those studies that have utilized already acquired routine data may be biased. The type and direction of this bias can be complex. For example, Cotter et al. (2012) using routinely collected data, concluded that the prevalence of dementia in Aboriginal Australians in the Northern Territory was not higher than non-Aboriginal prevalence. Using similar methodologies some years later the conclusion was that the Aboriginal population had markedly higher rates (Li et al., 2014). In the intervening period, a dementia awareness campaign coupled with the development of a culturally appropriate screening tool probably resulted in greater detection in routine care.
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 | 0.123 | 0.439 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.007 |
| Bibliometrics | 0.021 | 0.015 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.017 | 0.036 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.016 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".