A Comprehensive Review of Optimal Approaches to Co-Design in Health with First Nations Australians
Bibliographic record
Abstract
Background: Australia’s social, structural, and political context, together with the continuing impact of colonisation, perpetuates health care and outcome disparities for First Nations Australians. A new approach led by First Nations Australians is required to address these disparities. Co-design is emerging as a valued method for First Nations Australian communities to drive change in health policy and practice to better meet their needs and priorities. However, it is critical that co-design processes and outcomes are culturally safe and effective. Aims: This project aimed to identify the current evidence around optimal approaches to co-design in health with First Nations Australians. Methods: First Nations Australian co-led team conducted a comprehensive review to identify peer-reviewed and grey literature reporting the application of co-design in health-related areas by and with First Nations Australians. A First Nations Co-Design Working Group (FNCDWG) was established to guide this work and team.A Collaborative Yarning Methodology (CYM) was used to conduct a thematic analysis of the included literature. Results: After full-text screening, 99 studies were included. Thematic analysis elicited the following six key themes, which included 28 practical sub-themes, relevant to co-design in health with First Nations Australians: First Nations Australians leadership; Culturally grounded approach; Respect; Benefit to First Nations communities; Inclusive partnerships; and Evidence-based decision making. Conclusion: The findings of this review provide a valuable snapshot of the existing evidence to be used as a starting point to guide appropriate and effective applications of co-design in health with First Nations Australians.
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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 | 0.056 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".