Acknowledging and Promoting Indigenous Knowledges, Paradigms, and Practices Within Health Literacy-Related Policy and Practice Documents Across Australia, Canada, and New Zealand
Bibliographic record
Abstract
Enhancing health literacy can empower individuals and communities to take control over their health as well as improve safety and quality in healthcare. However, Indigenous health studies have repeatedly suggested that conceptualisations of health literacy are confined to Western knowledge, paradigms, and practices. The exploratory qualitative research design selected for this study used an inductive content analysis approach and systematic iterative analysis. Publicly available health literacy-related policy and practice documents originating from Australia, Canada, and New Zealand were analysed to explore the extent to which and the ways in which Indigenous knowledges are recognised, acknowledged, and promoted. Findings suggest that active promotion of Indigenous-specific health knowledges and approaches is limited and guidance to support recognition of such knowledges in practice is rare. Given that health services play a pivotal role in enhancing health literacy, policies and guidelines need to ensure that health services appropriately address and increase awareness of the diverse strengths and needs of Indigenous Peoples. The provision of constructive support, resources, and training opportunities is essential for Indigenous knowledges to be recognised and promoted within health services. Ensuring that Indigenous communities have the opportunity to autonomously conceptualise health literacy policy and practice is critical to decolonising health care.
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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 | 0.040 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".