Colonial drivers and cultural protectors of brain health among Indigenous peoples internationally
Bibliographic record
Abstract
Despite relatively higher rates of dementia among Indigenous populations internationally, research into drivers of disparities in brain health and cognitive function has tended to focus on modifiable risk factors over cultural understandings and contextual determinants. By seeking to characterize social and cultural factors that shape brain health and cognition in Indigenous populations, this mini scoping review expands prevailing schools of thought to include Indigenous knowledge systems. This reveals important gaps in culturally aligned care. It also reclaims horizons for research important to Indigenous Peoples that have garnered diminished attention in biomedical approaches. Twenty-three sources were included for data extraction. This synthesis of 23 sources includes health communication about dementia, health provider knowledge about Indigenous health, culturally relevant screening and assessment tools, and culturally grounded care models. Much of the focus is currently still on modifiable risk factors that reside at individual factors, whereas attention to wider social factors that impact populations is needed, as stressors through isolation, discrimination, and unequal care are widely reported. Going forward, identifying structural barriers to living well and recognizing the importance of connection to culture will benefit both Indigenous and non-Indigenous understandings of brain health.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".