Water quality and Inuit health: an examination of drinking water consumption, perceptions, and contamination in Rigolet, Canada
Bibliographic record
Abstract
Canadian Inuit have often reported concerns about the quality of their municipal drinking water; research has also shown that some Inuit communities experience some of the highest incidence rates of self-reported acute gastrointestinal illness (AGI) in Canada and globally. The goal of this thesis research was to investigate drinking water perceptions and consumption patterns, as well as water contamination and potential associations with AGI in the Inuit community of Rigolet, Canada. Three census cross-sectional surveys captured data on AGI, drinking water, and water storage (2012-2014); additionally, bacterial contamination of household drinking water was assessed alongside the 2014 survey. Concerns regarding the taste, smell, and colour of tap water were associated with lower odds of consuming tap water. The use of transfer devices (i.e. small bowls or measuring cups) was associated with household water contamination; while no water-related risk factors for AGI were identified, incidence of AGI was high compared with southern Canada. This thesis research provides a valuable contribution to the limited literature assessing drinking water and health in the Arctic. Ultimately, this work is intended to inform safe water management practices, as well as contextually appropriate drinking water interventions, risk assessments, and public health messaging in the Canadian Arctic.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".