Can Cochrane Reviews inform decisions to improve indigenous people's health?
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. Another editorial provides an overview: Improving health outcomes for Indigenous peoples: what are the challenges?Cochrane Reviews can provide valuable evidence to support an accountable decision-making process to improve Indigenous people's health.Such a process needs to consider community values, preferences, local needs, and resource use, as well as provide opportunities for feedback and debate.[1] In this editorial, we highlight strengths and limitations of systematic reviews in the context of Indigenous health, and we propose key steps to ensure systematic reviews are able to meet this challenge.We use the collective term 'Indigenous' when referring to first peoples, respectfully acknowledging the diversity and autonomy of different communities included in this broad term. Can Cochrane Reviews inform decisions to improve Indigenous people's health? (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 | 0.056 | 0.293 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.019 | 0.012 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.016 | 0.013 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".