Co-development of a risk assessment tool for use in First Nations water supply systems: A key step to water safety plan implementation
Bibliographic record
Abstract
Despite several years of targeted interventions, First Nations drinking water systems in Canada remain under-resourced and require substantial improvements in both infrastructure and management to provide communities with safe drinking water. The purpose of this study was to co-develop a risk assessment process integral to the water safety planning methodology to determine if proactive risk assessment provides a beneficial management tool for First Nations water systems. We co-developed a risk assessment web-application with First Nations stakeholders to identify hazards and assess risk in six Atlantic region First Nations communities. Using this application, we were able to successfully identify high-risk hazards in each community, both risks specific to individual systems, and risks common at a regional level. Through semi-structured interviews we identified the following benefits of a risk assessment web application: increased communication, data ownership and centralized data management. However, challenges remain, including current fragmented governance realities, and liability concerns associated with adopting a new risk management strategy. Successful adoption of proactive risk management strategies in First Nations communities will depend on strong co-development of risk assessment tools, transparent communication between stakeholders and clearly defined data ownership and management practices.
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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 | 0.093 | 0.135 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".