“Is there anything good about a water advisory?”: an exploration of the consequences of drinking water advisories in an indigenous community
Bibliographic record
Abstract
BACKGROUND: In Ontario, Canada, Indigenous communities experience some of the province's worst drinking water, with issues ranging from deteriorating water quality to regulatory problems and lack of support. When water is known, or suspected, to be unsafe for human consumption, communities are placed under a Drinking Water Advisory. Between 2004 and 2013, approximately 70% of all on-reserve communities in Ontario were under at least one Drinking Water Advisory. Despite the widespread impact of Drinking Water Advisories on health and wellbeing, little is known about First Nation individuals' perceptions and experiences living with a Drinking Water Advisory. This study presents information shared by members of a community who have lived with Boil Water Advisories on and off for many years, and a long-term Boil Water Advisory since 2017. The goal of this paper is to unpack and explore the Boil Water Advisories from the perspective of community members and provide considerations for current and future Boil Water Advisory management. METHODS: Methodological choices were driven by the principles of community-based participatory research. Two data collection methodologies were employed: hard copy surveys and interviews. RESULTS: Forty-four individuals (19.5%) completed a survey. Eight Elders and 16 key informants participated in 20 interviews. Respondents expressed varying degrees of uncertainty regarding protective actions to take while under a Boil Water Advisory. Further, 79% of men but only 46% of women indicated they always adhere to the Boil Water Advisory. Knowledge gaps that could lead to risky behaviours were also identified. Finally, Boil Water Advisories were demonstrated to have physical, financial, and time impacts on the majority of respondents. CONCLUSIONS: A direct outcome was the identification of a critical need to reinforce best practices for health protection through community education and outreach. More broadly, Chief and Council were able to use the findings to successfully advocate for improved drinking water for the community. Additionally, benefits of participatory research and community ownership include enhanced local research capacity, and increased awareness of, and desire for, research to inform decisions.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.011 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".