International Group for Indigenous Health Measurement: Recommendations for best practice for estimation of Indigenous mortality
Bibliographic record
Abstract
AIM: To provide a best practice guide on Indigenous mortality reporting based on recommendations from the International Group for Indigenous Health Measurement. METHOD: A workshop of the International Group for Indigenous Health Measurement was held in Montreal in 2013 during which best practices in determining Indigenous mortality were discussed. A subsequent discussion paper and draft recommendations were further refined at a meeting in Vancouver in 2014. A working group finalized this best practice guide in follow-up to the two meetings. OUTCOME: Ten final recommendations are made regarding identification, community engagement and ownership, data linkage, uncertainty in official statistics and a timeline for implementation. In this paper we review and discuss these recommendations drawing on examples of best practice in Australia, Canada, New Zealand and the United States of America and highlighting some shortcomings in the current practices of official statistical agencies.
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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 | 0.408 | 0.541 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.026 | 0.026 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.015 | 0.017 |
| Research integrity | 0.011 | 0.027 |
| Insufficient payload (model declined to judge) | 0.010 | 0.012 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".