Benchmarking Environmental Health Influences on Food Security in Very Remote Indigenous Communities in Australia: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Many factors including the impact of colonization and subsequent intergenerational trauma contribute to health inequalities for Aboriginal and Torres Strait Islander people, respectfully referred to as Indigenous Australians. The unacceptable health gap is higher for the Indigenous Australians living in very remote communities. Food insecurity-a lack of regular access to safe, nutritious, and affordable food-is influenced by both housing and retail environments. Ensuring that houses have functional and adequately maintained kitchens and access to affordable, healthy food are significant policy challenges for Australian governments; yet, little is known about these environmental health drivers in very remote areas. OBJECTIVE: This study aims to benchmark environmental health food security risk factors impacting 19 very remote Indigenous communities in Western Australia. Specific objectives include using digital apps (1) to assess the appropriateness and suitability of kitchens in houses (internal environment), (2) to assess the affordability of food and sanitary goods (external environment) compared with the nearest town and capital city, and (3) to identify residents' perceptions of appropriate kitchens. METHODS: The mixed methods eHealth study includes 3 approaches. The internal environment is assessed via an in-house audit of facilities used to prepare, store, and cook food to maintain Healthy Living Principle 4 using a customized digital app and a 5-minute face-to-face yarn with tenants (n=130). This provides lived experience perspectives to inform housing and store pricing policy recommendations. The external environment assesses retail practices and food item (n=97) and sanitation product (n=28) prices in remote community stores, extending Healthy Diets ASAP (Australian Standardized Affordability and Price) to compare the mean price per product, the whole diet, and sanitation goods with the nearest town and capital city. Descriptive statistics and frequencies will be reported for the audits, and thematic analysis of the interviews will be undertaken. RESULTS: Tenant interviews and data collection for the in-house and retail audits across the 19 communities will be undertaken by mid-2026, and the analysis will be completed by the end of 2026. Findings will be collated and triangulated to provide benchmark data for environmental health determinants of food security in very remote Western Australian communities. Preliminary findings will be shared with each community to support their advocacy, policy, and practices for timely maintenance of homes, suitable kitchen design, and store retail practices. CONCLUSIONS: This is the first study in Australia to explore the environmental health drivers of food insecurity in very remote Indigenous communities using digital technology from the perspectives of the tenant, in-house facilities, and in-store retail practices. The food security environmental health benchmarking will provide evidence for advocacy to promote culturally appropriate and practical solutions to improve living conditions and health of families in these areas. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/71697.
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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 | 0.071 | 0.049 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.059 | 0.012 |
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".