Can the gap in Aboriginal outcomes be explained by DOHaD
Bibliographic record
Abstract
In Australia, there are two distinct populations, each with vastly disparate health outcomes: Aboriginal and Torres Strait Islander People and non-Aboriginal Australians. Aboriginal Australians have significantly higher rates of health and socioeconomic disadvantage, and Aboriginal babies are also more likely to be born low birth weight or growth restricted. The Developmental Origins of Health and Disease (DOHaD) hypothesis advocates that a sub-optimal intrauterine environment, often manifested as diminished foetal growth, during critical periods of foetal development has the potential to alter the risk of non-communicable disease in the offspring. A better understanding of the role of the intrauterine environment and subsequent developmental programming, in response to both transgenerational and immediate stimuli, in Aboriginal Australians remains a relatively unexplored field and may provide insights into the prevailing health disparities between Aboriginal and non-Aboriginal children. This narrative review explores the role of DOHaD in explaining the ongoing disadvantage experienced by Aboriginal People in today's society through a detailed discussion of the literature on the association between foetal growth, as a proxy for the quality of the intrauterine environment, and outcomes in the offspring including perinatal health, early life development and childhood education. The literature largely supports this hypothesis and this review therefore has potential implications for policy makers not only in Australia but also in other countries that have minority and Indigenous populations who suffer disproportionate disadvantage such as the United States, Canada and New Zealand.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".