Health literacy and Australian Indigenous peoples: an analysis of the role of language and worldview
Bibliographic record
Abstract
This article delineates specific issues relating to health literacy for Indigenous Australians. Drawing on the extensive experience of the authors' work with Yolu people (of north-east Arnhem Land) and using one model for health literacy described in the international literature, various components of health literacy are explored, including fundamental literacy, scientific literacy, community literacy and cultural literacy. By matching these components to the characteristics of Yolu people, the authors argue that language and worldview form an integral part of health education methodology when working with Indigenous people whose first language is not English and who do not have a biomedical worldview in their history. Only through acknowledging and actively engaging with these characteristics of Indigenous people can all aspects of health literacy be addressed and health empowerment be attained. So what? The health literacy of Indigenous Australians can be improved by promoting the oral use of the people's first language in the health sphere and the use of in-depth language and worldview-based educational methodologies. It is also necessary to support Indigenous patients in decoding public health information and to place greater value on the Indigenous health worldview.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".