Self-reported health experience of Aboriginal and Torres Strait Islander peoples in the 2021 Australian population census
Bibliographic record
Abstract
Abstract For the first time, a question relating to certain long-term health conditions was asked in the 2021 Australian population census. The conditions included arthritis, asthma, cancer (including remission), dementia (including Alzheimer’s), diabetes (excluding gestational diabetes), heart disease (including heart attack or angina), kidney disease, lung condition (including COPD or emphysema), mental health condition (including depression or anxiety), and stroke. Respondents could indicate either none or any number of these conditions. In this paper, the data on these conditions have been analysed to compare the self-reported prevalence of diseases among the 23.4 million Australians who responded to this question (median age 39 years), of whom 3.2% were Aboriginal and Torres Strait Islander peoples (median age 24 years). Standardised Morbidity Ratios for each disease were calculated for the Aboriginal and Torres Strait Islander peoples by using the relevant age-disease specific morbidity rates for the total population as the ‘standard’. Findings of this research revealed that the prevalence of diseases was much higher among the Aboriginal and Torres Strait Islander peoples compared to the total population. The only exception was cancer, where the rates for the two groups were not much different. Gaps in prevalence of diseases in the two groups generally increased with age. The higher rates among the Aboriginal and Torres Strait Islander peoples emphasise the need for continued action on health equity and the development of more nuanced and focused initiatives to reduce the gaps between the Aboriginal and Torres Strait Islander peoples vis-à-vis the total population of Australia. Findings in this paper were consistent with similar studies (albeit largely surveys) on health of the Indigenous peoples in Canada and the Maoris in 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".