Embedding Authentic First Nations Content within Biomedical Science Curriculum
Bibliographic record
Abstract
The importance of incorporating First Nations content into curriculum has been widely recognised with significant progress in developing curricula and graduate attributes in several disciplines (Australian Government Department of Health, 2021; Page, et al., 2019), however substantial work remains, particularly in the sciences. \n \nA recent review of our Biochemistry of Nutrition curriculum identified an opportunity to incorporate authentic First Nations food and health content. Through collaboration with the University of Southern Queensland’s Elder in Residence, a new module was developed. The module, built around a traditional yarning circle experience, shared First Nations knowledge of culture, nutrition, and medicine. This was supported by lectorials and other content, including the importance of Indigenous research governance. \n \nAs we also recognised the need to introduce First Nations content vertically across the curriculum, we further collaborated with a First Nations health expert to integrate and deliver topics such as historical policies, health perspectives, and cultural safety into our first-year foundational Biomedical Science course. \n \nStudent feedback on these enhancements has been positive and the yarning circle approach to learning attracted substantial media attention. \n \nBy forming collaborations with local First Nations leaders, we have developed authentic First Nations content that has strengthened student knowledge and graduate preparation for work in the health and research fields.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.011 |
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".