Aboriginal and Torres Strait Islander people in Australia, education and health literacy
Bibliographic record
Abstract
Health literacy is a vital tool to build health knowledge and enable empowerment in health decision making at a community and individual level. There are different views of what constitutes health literacy with the most inclusive addressing broadly the skills and competencies required “to seek out, comprehend, evaluate, and use health information and concepts to make informed choices, reduce health risks, and increase quality of life” (Zarcadoolas 2005). Poor health literacy has been shown to impact health seeking behaviour, access and awareness to preventive health campaigns and adherence with treatment. Populations at risk of poor health have lower health literacy and this is compounded by lower socioeconomic status, lower education levels and, where language and cultural differences exist, these disparities may be magnified (Shaw 2008). Health literacy needs to consider both preventative health practices as well as treatment of identified conditions. While we know that poor health literacy does impact health seeking behaviour, access and awareness to preventive health campaigns and adherence with treatment, we seek and advocate solutions to improving health literacy, which are culturally appropriate and also support Indigeniety. We recognise the need to do both; otherwise gains in one area may be countered by lost ground in other areas with overall adverse consequences for Indigenous people and Australians as a whole. To do otherwise, produces a more unwell, inequitable Australian society.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".