Ngununggula: The story of a cancer care team for aboriginal people
Bibliographic record
Abstract
In Dharawal Country in regional New South Wales, a small and powerful team provides cancer prevention, screening, support and care for Australian Aboriginal people, their families and communities. In keeping with Aboriginal practices and values, their uniquely holistic approach encompasses everything from food security and finding childcare, to support at diagnosis, surgical, radiation or chemo treatment, through to holding funerals, facilitating yarning groups, and Ceremony for survivors of cancer and their carers. The team created a manual for Aboriginal Health Workers, and other staff of Aboriginal Community Controlled Health Services, together with training webinars, and modules. The program is also designed for Aboriginal Liaison Officers and Palliative Care Workers who work in hospitals. The book and the training modules are called Ngununggula. The name, from the Gundungurra language, means working and walking together. “We’ll make ourselves available to anyone that wants to tread this path because we know all the pitfalls. We’ve learned them. We’ve tripped and had to climb out of them again. Anyone that wants the shortcuts—more learning, less pain—here they are. We want to share and help. I want the message to get out all over the place. I want to share the resources, to support anyone else who wants to run programs or build a team like we do.” Kyla Wynn, Counsellor/Co-ordinator Cancer Care Team, Illawarra Aboriginal Medical Service. Partners include: Aboriginal Health and Medical Research Council, Illawarra Aboriginal Medical Service, University of Sydney, University of Wollongong, Menzies School of Health Research.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.056 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.022 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".