Putting Indigenous Cultures and Indigenous Knowledges Front and Centre to Clinical Practice: Katherine Hospital Case Example
Bibliographic record
Abstract
The inclusion of Indigenous cultures, known as the cultural determinants of health, in healthcare policy and health professional education accreditation and registration requirements, is increasingly being recognised as imperative for improving the appalling health and well-being of Indigenous Australians. These inclusions are a strengths-based response to tackling the inequities in Indigenous Australians' health relative to the general population. However, conceptualising the cultural determinants of health in healthcare practice has its contextual challenges, and gaps in implementation evidence are apparent. In this paper, we provide a case example, namely the Katherine Hospital, of how healthcare services can implement the cultural determinants of health into clinical practice. However, to be effective, health professionals must concede that Australia's Indigenous peoples' knowledges involving cultural ways of being, knowing and doing must co-exist with western and biomedical knowledges of health practice. We use the Katherine Hospital ABC Radio National Background Briefing interview, which was mentioned by two research participants in a 2020 study, as an example of good practice that we can learn from. Additionally, the six Aboriginal and Torres Strait Islander Health actions contained in the 2nd Edition of the Australian National Safety and Quality Health Service Standards provide governance and accountability examples of how to enable Indigenous people's cultures and their knowledges in the provision of services. The role of non-Indigenous clinical allies and accomplices is imperative when embedding and enacting Indigenous Australians' cultures in service systems of health. When Indigenous Peoples access mainstream hospitals, deep self-reflection by allies and accomplices is necessary to enable safe, quality care, and treatment that is culturally safe and free from racism. Doing so can increase cultural responsiveness free of racism, thereby reducing the inherent power imbalances embedded within mainstream health services.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".