Domestic water security in the Arctic: A scoping review
Bibliographic record
Abstract
INTRODUCTION: More than 50 million people living in the Arctic nations remain without access to safely managed drinking water services. Remote northern communities, where large numbers of Indigenous peoples live, are disproportionally affected. Recent research has documented water and health-related problems among Indigenous communities, including poor water quality and insufficient quantities of water. OBJECTIVE: The objective of this scoping review is to examine the extent of available water security evidence as well as identify research gaps and intervention priorities to improve access to domestic water in the Arctic and Subarctic regions of the eight Arctic nations (Canada, the Kingdom of Denmark (Greenland), Finland, Iceland, Norway, Sweden, Russia, and the United States (Alaska)). METHODS: An extensive literature review was conducted to retrieve relevant documentation. Arctic & Antarctic Regions, Compendex, Geobase, Georef, MEDLINE and Web of Science databases were searched to identify records for inclusion. The initial searches yielded a total of 1356 records. Two independent reviewers systematically screened identified records using selection criteria. Descriptive analyses were used to summarize evidence of included studies. RESULTS: A total of 55 studies, mostly conducted in Canada and the United States, were included and classified by four predetermined major dimensions: 1) Water accessibility and availability; 2) Water quality assessment; 3) Water supply and health; 4) Preferences and risk perceptions. CONCLUSIONS: This scoping review used a global approach to provide researchers and stakeholders with a summary of the evidence available regarding water security and domestic access in the Arctic. Culturally appropriate health-based interventions are necessary to ensure inclusive water services and achieve the Sustainable Development Goals (SDG) targets for universal access to water.
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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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.021 | 0.022 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".