Drinking Water Advisories and Pathways Forward
Bibliographic record
Abstract
Background: Water quality in on-reserve Indigenous communities in Ontario is concerning, with issues ranging from deteriorating water quality to issues with regulation and support. As a result, many communities are placed under a Drinking Water Advisory (DWA), which, at its most severe, indicates water is not safe for use or consumption. Between 2004 and 2013, approximately 70% of all on-reserve communities in Ontario were under at least one DWA. While designed to protect physical health, DWAs have widespread impacts on health and wellbeing. However, little is known about First Nation individuals’ thoughts, feelings, and experiences living under a DWA. Purpose: To unpack and explore the broad impacts of a Boil Water Advisory (BWA) from the perspective of community members on a reserve in Northern Ontario and provide considerations for current and future BWA management. Methods: Methodological choices were driven by the principles of community-based participatory research. Questionnaires and interviews were used to collect data. Two hundred and twenty-six questionnaires were distributed. Fifteen Elders and 22 key informants (KIs) were contacted for an interview. Results: Forty-four (19.5%) individuals completed a questionnaire. Eight Elders and 16 KIs participated in 20 interviews. Questionnaire sections were used to frame the data analysis, which fell under five major themes: 1) Community Context; 2) Knowledge of BWAs; 3) Living Under a BWA; 4) Water and Health; and, 5) Pathways Forward. Conclusions: Responses illuminated gaps where education surrounding best practices for protecting health would be crucial moving forward. They also highlighted that women are an important target audience for education. Points also emerged where communication with community members and stakeholders would be key to understanding the impacts and outcomes of DWAs. Additionally, this community, and others experiencing a DWA, should consider greater involvement in water management by younger individuals, to ensure that all perspectives are adequately represented.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.001 |
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".