A review of drinking water quality issues in remote and indigenous communities in rich nations with special emphasis on Australia
Bibliographic record
Abstract
This review paper examines the drinking water quality issues in remote and Indigenous communities, with a specific emphasis on Australia. Access to clean and safe drinking water is vital for the well-being of Indigenous communities worldwide, yet numerous challenges hinder their ability to obtain and maintain water security. This review focuses on the drinking water-related issues faced by Indigenous populations in countries such as the United States, Canada, New Zealand, and Australia. In the Australian context, remote and Indigenous communities encounter complex challenges related to water quality, including microbial and chemical contamination, exacerbated by climate change effects. Analysis of water quality trends in Queensland, New South Wales, Western Australia, and the Northern Territory reveals concerns regarding various pollutants with very high concentrations in the source water leading to levels exceeding recommended drinking water limits such as hardness, turbidity, fluoride, iron, and manganese levels after limited treatment facilities available in these communities. Inadequate water quality and quantity contribute to adverse health effects, particularly among Indigenous populations who may resort to sugary beverages. Addressing these challenges requires comprehensive approaches encompassing testing, funding, governance, appropriate and sustainable treatment technologies, and cultural considerations. Collaborative efforts, risk-based approaches, and improved infrastructure are essential to ensure equitable access to clean and safe drinking water for remote and Indigenous communities, ultimately improving health outcomes and promoting social equity.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".