Development of Key Principles and Best Practices for Co-Design in Health with First Nations Australians
Bibliographic record
Abstract
BACKGROUND: While co-design offers potential for equitably engaging First Nations Australians in findings solutions to redressing prevailing disparities, appropriate applications of co-design must align with First Nations Australians' culture, values, and worldviews. To achieve this, robust, culturally grounded, and First Nations-determined principles and practices to guide co-design approaches are required. AIMS: This project aimed to develop a set of key principles and best practices for co-design in health with First Nations Australians. METHODS: A First Nations Australian co-led team conducted a series of Online Yarning Circles (OYC) and individual Yarns with key stakeholders to guide development of key principles and best practice approaches for co-design with First Nations Australians. The Yarns were informed by the findings of a recently conducted comprehensive review, and a Collaborative Yarning Methodology was used to iteratively develop the principles and practices. RESULTS: A total of 25 stakeholders participated in the Yarns, with 72% identifying as First Nations Australian. Analysis led to a set of six key principles and twenty-seven associated best practices for co-design in health with First Nations Australians. The principles were: First Nations leadership; Culturally grounded approach; Respect; Benefit to community; Inclusive partnerships; and Transparency and evaluation. CONCLUSIONS: Together, these principles and practices provide a valuable starting point for the future development of guidelines, toolkits, reporting standards, and evaluation criteria to guide applications of co-design 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 | 0.430 | 0.365 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.011 | 0.027 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.009 | 0.025 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".