HIV diagnoses in indigenous peoples: comparison of Australia, Canada and New Zealand
Bibliographic record
Abstract
In industrial countries, a number of factors put indigenous peoples at increased risk of HIV infection. National surveillance data between 1999 and 2008 provided diagnoses for Aboriginal and Torres Strait Islanders (Australia), First Nations, Inuit and Métis (Canada excluding Ontario and Quebec) and Māori (New Zealand). Each country provided similar data for a non-indigenous comparison population. Direct standardisation used the 2001 Canadian Aboriginal male population for comparison of five-year diagnosis rates in 1999-2003 and 2004-2008. Using the general population as denominators, we report diagnosis ratios for presumed heterosexual transmission, men who have sex with men (MSM) and intravenous drug users (IDU). Age standardised HIV diagnosis rates in indigenous peoples in Canada in 2004-2008 (178.1 and 178.4/100 000 for men and women respectively) were higher than in Australia (48.5 and 12.9/100 000) and New Zealand (41.9 and 4.3/100 000). Higher HIV diagnosis rates related to heterosexual contact among Aboriginal peoples, especially women, in Canada confirm a widening epidemic beyond the conventional risk groups. This potential of a generalised epidemic requires urgent attention in Aboriginal communities; available evidence can inform policy and action by all stakeholders. Although less striking in Australia and New Zealand, these findings may be relevant to indigenous peoples in other countries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".