Improving health outcomes for indigenous peoples: What are the challenges?
Bibliographic record
Abstract
This editorial accompanies a series of Cochrane Library Special Collections on the health of Indigenous peoples in Australia, Canada, and New Zealand, focusing on diabetes, fetal alcohol syndrome disorders, and suicide prevention. A second editorial asks Can Cochrane Reviews inform decisions to improve Indigenous people's health?The International Day of the World's Indigenous Peoples (www.un.org/en/events/indigenousday) is commemorated on 9 August each year.This day reminds us all to pause and question what progress has been achieved from the previous year.This year's theme, "Post 2015 agenda: Ensuring Indigenous peoples' health and well-being", is important for us all. Improving health outcomes for Indigenous peoples: what are the challenges? (Editorial) 1
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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 | 0.051 | 0.209 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.007 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.017 | 0.016 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".