INDIGENOUS EXPLANATORY MODELS OF DEMENTIA: RESULTS FROM HEALTHY OLDER ADULTS IN NORTH AMERICA
Bibliographic record
Abstract
Abstract Explanatory models of illness are important concepts in understanding a person’s beliefs about an illness and the social meaning that is attributed to that illness. This project seeks to understand an explanatory model of Alzheimer’s disease and related dementias (ADRD) among healthy older adults in four diverse Indigenous communities in the US (Minnesota, Wisconsin) and Canada (Ontario). Individual interviews (n=37) were conducted with healthy older adults whose age ranged from 49-79 years (M = 65.5) and were majority female (n=23, 62.2%). Four qualitative analysts conducted team-based coding, double-coding 30% of interviews to ensure intercoder agreement Kappa of ≥.80. A priori codes developed based on Kleinman’s explanatory model resulted in seven content areas: perceptions of ADRD, causes of ADRD, naming ADRD, ADRD treatment and/or care, diagnosis of ADRD/memory concerns, ADRD prevention, and fear/worry regarding ADRD. Themes under these content areas were varied but are reflective of holistic worldviews of health including physical, mental, cultural, and spiritual impacts. For example, participants report that changes associated with dementia align with completing the circle of life, so Elders may share similar traits as infants in terms of help and support. The consequences of ADRD are felt at both interpersonal and community levels, with the loss of traditional knowledge, generational memory, and stories. The default form of ADRD care is within families, with participants reporting that people should live as long as possible at home. This study provides comprehensive information about healthy older adults’ attitudes and perceptions of ADRD in Indigenous communities.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".