Canadian Alliance for Healthy Hearts and Minds: First Nations Cohort Study Rationale and Design
Bibliographic record
Abstract
BACKGROUND: This is the first national indigenous cohort study in which a common, in-depth protocol with a common set of objectives has been adopted by several indigenous communities across Canada. OBJECTIVES: The overarching objective of the Canadian Alliance for Healthy Hearts and Minds (CAHHM) cohort is to investigate how the community-level environment is associated with individual health behaviors and the presence and progression of chronic disease risk factors and chronic diseases such as cardiovascular disease (CVD) and cancer. METHODS: CAHHM aims to recruit approximately 2,000 First Nations indigenous individuals from up to nine communities across Canada and have participants complete questionnaires, blood collection, physical measurements, cognitive assessments, and magnetic resonance imaging (MRI). RESULTS: Through individual- and community-level data collection, we will develop an understanding of the specific role of the socioenvironmental, biological, and contextual factors have on the development of chronic disease risk factors and chronic diseases. CONCLUSIONS: Information collected in the indigenous cohort will be used to assist communities to develop local management strategies for chronic disease, and can be used collectively to understand the contextual, environmental, socioeconomic, and biological determinants of differences in health status in harmony with First Nations beliefs and reality.
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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 | 0.022 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".