Indigenous Birth Outcomes in Australia, Canada, New Zealand and the United States - an Overview~!2010-01-14~!2010-04-16~!2010-07-06~!
Bibliographic record
Abstract
Objective-To review Indigenous infant mortality, stillbirth, birth weight, and preterm birth outcomes in Australia, Canada, New Zealand and the United States.Methods-Systematic searches of published literature and a review and assessment of existing perinatal surveillance systems were undertaken.Where possible, within country comparisons of Indigenous to non-Indigenous birth outcomes are included.Results-Indigenous/non-Indigenous infant mortality rate ratios range from 1.6 to 4.0.Stillbirth rates, where data are available, are also uniformly higher for Indigenous people.In all four countries, the disparities in Indigenous/non-Indigenous infant mortality rate ratios are most marked in the post-neonatal period.With few exceptions, the rates of leading causes of infant mortality are higher among Indigenous infants than non-Indigenous infants within all four countries.In most cases, rates of small for gestational age and preterm birth were also elevated for Indigenous compared to non-Indigenous infants.Conclusions-There are significant disparities in Indigenous/non-Indigenous birth outcomes in Australia, Canada, New Zealand and the United States.These Indigenous/non-Indigenous birth outcome disparities fit the criteria for health inequities, as they are not only unnecessary and avoidable, but also unfair and unjust.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".