Education of First Nations Australians about cancer—what evidence is there around its effectiveness? A narrative review
Bibliographic record
Abstract
Poorer cancer prevention and control outcomes for First Nations Australians have resulted in a need for improved health literacy, that is their capacity as individuals to access, understand, and use information in ways that promote and maintain good health. This narrative review explores the evidence on how education programs work to improve First Nations Australians' understanding of cancer, and how this leads to more effective use of prevention, screening and treatment services. Limited to the Australian context, a bibliographic search using terms structured around four main concepts: Aboriginal, Cancer, Australia, and Intervention (health literacy, health promotion) was undertaken in May 2024 for publications from January 2000. Studies that evaluated interventions or initiatives aimed to educate (that is, to increase awareness, knowledge and influence behaviours) across the cancer journey (excluding palliative care) were included. Fourteen evaluated initiatives drawn from peer-reviewed papers, published reports and peak body case studies were included in the review. All included papers were assessed with three major areas considered important for determining the effectiveness of cancer education for First Nations Australians: 1) acceptability: delivery in a format that is acceptable to community and health services, 2) impact: having a positive impact on people's access to and understanding of the cancer pathway and/or increases intended or actual participation, and 3) viability: successfully navigating the challenges of viably implementing, sustaining and scaling up an initiative. This review highlights several factors that contribute to the effectiveness of cancer education initiatives targeting First Nations Australians: culturally tailored initiatives, co-design and consultation during development, capacity building to create supportive, trusting environments for accessible cancer education; local ownership and empowerment, integration with existing healthcare systems, flexibility and multi-pronged approaches. Challenges and areas for further efforts in developing cancer health literacy are also described. Relatively few initiatives are evaluated: most of these have focussed on screening and treatment.
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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 | 0.011 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".