Factors associated with drinking and being satisfied with tap water in Indigenous communities in Saskatchewan, Canada
Bibliographic record
Abstract
Previous studies have described concerns regarding tap water in Indigenous communities, yet there is little information on participants who report drinking their tap water and being satisfied with its quality. This study undertaken with members of 8 Indigenous communities in Saskatchewan, Canada, and identified factors associated with both the decision to drink tap water at home and being satisfied with its quality. We examined the importance of factors such as individual attributes, experiences, attitudes, household and community-based variables. Less than one-quarter of participants (23.4%) drank tap water and were satisfied with its quality. Individuals who did not boil tap water (odds ratio [OR] = 5.76, 95% confidence interval [CI] = 1.68-19.8), those who did not experience tap water odour (OR = 2.38, 95% CI = 1.26-4.50) and participants living in communities away from urban centres (OR = 2.74, 95% CI = 1.63-4.51) were more likely to drink and be satisfied with their tap water. Concerns about the environment had the most impact on community members aged 55+ years. Those not reporting concerns about environmental problems affecting water (OR = 11.4, 95% CI = 3.10-42.2) were much more likely to drink and be satisfied with their tap water. Programmes to improve water quality, reduce the need for boil water advisories and increase community confidence in the environment could improve tap water satisfaction and consumption.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".