Early-life origins of disparities in chronic diseases among Indigenous youth: pathways to recovering health disparities from intergenerational trauma
Bibliographic record
Abstract
Indigenous women and children experience some of the most profound health disparities globally. These disparities are grounded in historical and contemporary trauma secondary to colonial atrocities perpetuated by settler society. The health disparities that exist for chronic diseases may have their origins in early-life exposures that Indigenous women and children face. Mechanistically, there is evidence that these adverse exposures epigenetically modify genes associated with cardiometabolic disease risk. Interventions designed to support a resilient pregnancy and first 1000 days of life should abrogate disparities in early-life socioeconomic status. Breastfeeding, prenatal care and early child education are key targets for governments and health care providers to start addressing current health disparities in cardiometabolic diseases among Indigenous youth. Programmes grounded in cultural safety and co-developed with communities have successfully reduced health disparities. More works of this kind are needed to reduce inequities in cardiometabolic diseases among Indigenous women and children worldwide.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".