Stakeholder perceptions of policy implementation for Indigenous health and cultural safety: A study of Australia's ‘Closing the Gap’ policies
Bibliographic record
Abstract
Abstract Indigenous peoples in Australia and similar colonised countries are subject to racism and systemic socioeconomic disadvantages, resulting in worse health outcomes compared to non‐Indigenous counterparts. Such inequities persist despite governments’ attempts to reduce them. Since 2008, Australian governments have committed to a national ‘Closing the Gap’ (CTG) to reduce inequities in health, education, and employment outcomes between Aboriginal and Torres Strait Islander peoples and other Australians, but with limited success. We applied policy theory and a cultural safety framework developed for the research to analyse stakeholder perceptions of CTG policy implementation between 2008 and 2019. We identified policy‐shaping ideas and policy incoherence in the environment surrounding CTG policy that obstructed culturally safe policy. Top‐down, prescriptive modes of implementation were also a barrier. However, Indigenous‐led policy partnerships and community‐controlled services in the health sector have met principles of cultural safety. Identifying these strengths and weaknesses points to ways in which implementation of CTG policies can be improved to achieve cultural safety and reduce Indigenous health inequities. These results may hold lessons for similar countries such as the United States, New Zealand, and Canada.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".