Priority Setting in Indigenous Health: Why We Need an Explicit Decision Making Approach
Bibliographic record
Abstract
Indigenous Australians have significantly poorer health outcomes than the non-Indigenous population worldwide. The Australian government has increased its investment in Indigenous health through the "Closing the Health Gap" initiative. Deciding where to invest scarce resources so as to maximize health outcomes for Indigenous peoples may require improved priority setting processes. Current government practice involves a mix of implicit and explicit processes to varying degrees at the macro and meso decision making levels. In this article, we argue that explicit priority setting should be emphasized in Indigenous health, as it can ensure that the decision making process is accountable, systematic, and transparent. Following a review of the literature, we outline four key issues that need to be considered for explicit priority setting: developing an Indigenous health "constitution," strengthening the evidence base, selecting mechanisms for priority setting, and establishing appropriate incentives and institutional structure. We then summarize our findings into a checklist that can help a decision makers ensure that explicit priority setting is undertaken in Indigenous health. By addressing these key issues, the benefits of an explicit approach, which include increased efficiency, equity, and use of evidence, can be realized, thereby maximizing Indigenous health outcomes.
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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 | 0.230 | 0.217 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.030 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.008 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 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".