Converging approaches to understanding early onset familial Alzheimer disease: A First Nation study
Bibliographic record
Abstract
OBJECTIVES: In 2007, a novel pathogenic genetic mutation associated with early onset familial Alzheimer disease was identified in a large First Nation family living in communities across British Columbia, Canada. Building on a community-based participatory study with members of the Nation, we sought to explore the impact and interplay of medicalization with the Nation's knowledge and approaches to wellness in relation to early onset familial Alzheimer disease. METHODS: We performed a secondary content analysis of focus group discussions and interviews with 48 members of the Nation between 2012 and 2013. The analysis focused specifically on geneticization, medicalization, and traditional knowledge of early onset familial Alzheimer disease, as these themes were prominent in the primary analysis. RESULTS: We found that while biomedical explanations of disease permeate the knowledge and understanding of early onset familial Alzheimer disease, traditional concepts about wellness are upheld simultaneously. CONCLUSION: The analysis brings the theoretical framework of "two-eyed seeing" to the case of early onset familial Alzheimer disease for which the contributions of different ways of knowing are embraced, and in which traditional and western ways complement each other on the path of maintaining wellness in the face of progressive neurologic disease.
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".