Gaps in Indigenous disadvantage not closing: a census cohort study of social determinants of health in Australia, Canada, and New Zealand from 1981–2006
Bibliographic record
Abstract
BACKGROUND: Australia, Canada, and New Zealand are all developed nations that are home to Indigenous populations which have historically faced poorer outcomes than their non-Indigenous counterparts on a range of health, social, and economic measures. The past several decades have seen major efforts made to close gaps in health and social determinants of health for Indigenous persons. We ask whether relative progress toward these goals has been achieved. METHODS: We used census data for each country to compare outcomes for the cohort aged 25-29 years at each census year 1981-2006 in the domains of education, employment, and income. RESULTS: The percentage-point gaps between Indigenous and non-Indigenous persons holding a bachelor degree or higher qualification ranged from 6.6% (New Zealand) to 10.9% (Canada) in 1981, and grew wider over the period to range from 19.5% (New Zealand) to 25.2% (Australia) in 2006. The unemployment rate gap ranged from 5.4% (Canada) to 16.9% (Australia) in 1981, and fluctuated over the period to range from 6.6% (Canada) to 11.0% (Australia) in 2006. Median Indigenous income as a proportion of non-Indigenous median income (whereby parity = 100%) ranged from 77.2% (New Zealand) to 45.2% (Australia) in 1981, and improved slightly over the period to range from 80.9% (Canada) to 54.4% (Australia) in 2006. CONCLUSIONS: Australia, Canada, and New Zealand represent nations with some of the highest levels of human development in the world. Relative to their non-Indigenous populations, their Indigenous populations were almost as disadvantaged in 2006 as they were in 1981 in the employment and income domains, and more disadvantaged in the education domain. New approaches for closing gaps in social determinants of health are required if progress on achieving equity is to improve.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".