Towards an Aboriginal Knowledge Place: Cultural Practices as a Pathway to Wellness in the Context of a Tertiary Hospital
Bibliographic record
Abstract
The Indigenous community in Australia is beset by extraordinary disadvantage, with health outcomes that are substantially worse than those of non-Indigenous citizens. This issue has consequently been the subject of voluminous health research that has given rise to a range of affirmative action policies progressively implemented over the past decade. Statistics, however, remain dire. This paper argues that new models of research practice and policy are required that are inclusive of Indigenous ways of knowing, doing, and being. It proposes a new framework to promote wellness in urban hospitals for Aboriginal young people and their families modelled on equal, 2-way dialogue between Western and Indigenous ways of doing health. Cultural safety is an essential starting point, but a range of other practices is proposed including oversight by a board of Elders, inclusion of traditional healers in treatment teams, and “space, place, and base” within the hospital building and its grounds so that it can be used as a site for culturally engaged Indigenous outpatient care. Practice approaches that embed culture into assessment, formulation, and treatment are being trialled by the authors of this paper, three of whom have Aboriginal heritage. Together the authors are working toward building an Aboriginal Knowledge Place within the major teaching hospital where they work.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.023 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".