Anishinaabe healthy brain aging: traditional knowledge teachings represented in works of art
Bibliographic record
Abstract
Dementia is a prevalent and growing concern among Indigenous communities. Research on the lived experience, culture, and context of the dementia journey has improved our understanding of Indigenous Peoples' experiences. This research study elucidates the deep understanding of healthy brain aging from interviews with seven Anishinaabe/Ojibwe Traditional Knowledge Sharers from Manitoulin Island in Ontario, Canada. Using a community-based participatory research (CBPR) approach and Indigenous methodologies, researchers worked with a Community Advisory Council (CAC) to develop the study. Interviews with Traditional Knowledge Sharers were recorded, transcribed, and shared with a local Anishinaabe/Ojibwe artist for his interpretation and artistic representation of the key healthy brain aging teaching that arose from the interview. The artist developed six paintings based on these teachings for Anishinaabe/Ojibwe people, titled: Perseverance, Anishinaabe Cognition, Presence, Healing Step, Benevolence, and Linkages. A seventh painting was also developed, with the shared teaching of Bebaminojmat-one who goes around and shares their healing gifts. We describe these teachings and the cross-cutting themes that arose from the post-analysis. This study contributes to the field of aging by revealing cultural and philosophical concepts related to healthy brain aging specifically for Anishinaabe/Ojibwe people. The findings can be used to guide healthcare providers in supporting healthy brain aging in culturally meaningful ways and developing culturally safe health education interventions. Artistic analysis and representation proved to be an effective vehicle for exploring cultural values central to the development of programs and education aimed at addressing existing brain health disparities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".