Pathways between foodways and wellbeing for First Nations Australians
Bibliographic record
Abstract
BACKGROUND: Supporting the health and wellbeing of Aboriginal and Torres Strait Islander peoples (hereafter respectfully referred to as First Nations peoples) is a national priority for Australia. Despite immense losses of land, language, and governance caused by the continuing impact of colonisation, First Nations peoples have maintained strong connections with traditional food culture, while also creating new beliefs, preferences, and traditions around food, which together are termed foodways. While foodways are known to support holistic health and wellbeing for First Nations peoples, the pathways via which this occurs have received limited attention. METHODS: Secondary data analysis was conducted on two national qualitative datasets exploring wellbeing, which together included the views of 531 First Nations peoples (aged 12-92). Thematic analysis, guided by an Indigenist research methodology, was conducted to identify the pathways through which foodways impact on and support wellbeing for First Nations peoples. RESULTS AND CONCLUSIONS: Five pathways through which wellbeing is supported via foodways for First Nations peoples were identified as: connecting with others through food; accessing traditional foods; experiencing joy in making and sharing food; sharing information about food and nutrition; and strategies for improving food security. These findings offer constructive, nationally relevant evidence to guide and inform health and nutrition programs and services to harness the strengths and preferences of First Nations peoples to support the health and wellbeing of First Nations peoples more effectively.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".