Drinking water quality in Indigenous communities in Canada and health outcomes: a scoping review
Bibliographic record
Abstract
BACKGROUND: Many Indigenous communities in Canada live with high-risk drinking water systems and drinking water advisories and experience health status and water quality below that of the general population. A scoping review of research examining drinking water quality and its relationship to Indigenous health was conducted. OBJECTIVE: The study was undertaken to identify the extent of the literature, summarize current reports and identify research needs. DESIGN: A scoping review was designed to identify peer-reviewed literature that examined challenges related to drinking water and health in Indigenous communities in Canada. Key search terms were developed and mapped on five bibliographic databases (MEDLINE/PubMED, Web of Knowledge, SciVerse Scopus, Taylor and Francis online journal and Google Scholar). Online searches for grey literature using relevant government websites were completed. RESULTS: Sixteen articles (of 518; 156 bibliographic search engines, 362 grey literature) met criteria for inclusion (contained keywords; publication year 2000-2015; peer-reviewed and from Canada). Studies were quantitative (8), qualitative (5) or mixed (3) and included case, cohort, cross-sectional and participatory designs. In most articles, no definition of "health" was given (14/16), and the primary health issue described was gastrointestinal illness (12/16). Challenges to the study of health and well-being with respect to drinking water in Indigenous communities included irregular funding, remote locations, ethical approval processes, small sample sizes and missing data. CONCLUSIONS: Research on drinking water and health outcomes in Indigenous communities in Canada is limited and occurs on an opportunistic basis. There is a need for more research funding, and inquiry to inform policy decisions for improvements of water quality and health-related outcomes in Indigenous communities. A coordinated network looking at First Nations water and health outcomes, a database to store and create access to research findings, increased funding and time frames for funding, and more decolonizing and community-based participatory research aimed at understanding the relationship between drinking water quality and health outcomes in First Nations communities in Canada are needed.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.017 | 0.034 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".