10 years of preventive health in Australia. Part 2 – centring First Nations sovereignty
Bibliographic record
Abstract
As First Nations public health professionals, we critically examine the National Preventive Health Strategy 2021-2030 (NPHS) and its shortcomings in addressing the structural determinants of health inequities affecting Aboriginal and Torres Strait Islander peoples (hereafter respectfully, First Nations peoples). Although the NPHS aspires to a systems-based and equitable approach, we argue that it fails to meaningfully engage with the enduring impacts of colonisation, systemic racism, and intergenerational trauma. By focusing predominantly on individual behavioural risk factors, the strategy neglects the broader sociopolitical and cultural contexts that continue to drive poorer health outcomes in our communities. True progress in preventive health requires a fundamental shift - one that centres First Nations self-determination; embeds our ways of knowing, being, and healing; and invests in community-led solutions. We call for the re-Indigenisation of the health system, not as a gesture of inclusion, but as an assertion of our sovereignty, knowledge, and leadership in shaping our own health futures. We conclude with a series of actionable recommendations for policymakers grounded in structural reform and driven by the urgent need for systems transformation led by, and accountable to, First Nations peoples.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| 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".