Navigating two worlds: developing a learning map to visualise the knowledge and skills required for culturally informed shared decision making with Aboriginal people in New South Wales Australia
Bibliographic record
Abstract
Finding Your Way is a shared decision making (SDM) resource created with and for Aboriginal people in 2021. It is the only culturally adapted SDM resource for Aboriginal people in Australia and one of few examples developed with First Nations people internationally. A two-round modified e-Delphi approach, incorporating yarning methods, was used to gather expert opinions and reach a consensus on the capabilities (knowledge and skills) required to effectively use Finding Your Way and engage in SDM with Aboriginal people. 29 predefined capabilities were gleaned from the research evidence and yarning sessions to form the basis of the e-Delphi. 138 panel members completed round one of the e-Delphi between 19/01/2023 and 27/01/2023, and 113 completed round two between 09/02/2023 and 20/02/2023. There was 82% panel member retention rate across the two-e Delphi rounds and the consensus threshold was 75% strongly agree. Consensus was reached for ten capabilities, and a learning map was developed to reflect Aboriginal valuing, being, knowing and doing as represented in the Aboriginal 8 Ways of Learning pedagogy. Cultural imagery was used to create the learning map representing key knowledge and skills required ton use Finding Your Way, presenting this information in a symbolic and non-linear way.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".