Microbial water quality communication: public and practitioner insights from British Columbia, Canada
Bibliographic record
Abstract
This work examines the communication interactions of water suppliers and health authorities with the general public regarding microbial source water quality for recreational and drinking water. We compare current approaches to risk communication observable in British Columbia (BC), Canada, with best practices derived from the communications literature, finding significant gaps between theory and practice. By considering public views and government practices together, we identify key disconnects, leading to the conclusion that at present, neither the public's needs nor public health officials' goals are being met. We find: (1) there is a general lack of awareness and poor understanding by the public of microbial threats to water and the associated health implications; (2) the public often does not know where to find water quality information; (3) public information needs are not identified or met; (4) information sharing by authorities is predominantly one-way and reactive (crisis-oriented); and (5) the effectiveness of communications is not evaluated. There is a need for both improved public understanding of water quality-related risks, and new approaches to ensure information related to water quality reaches audiences. Overall, greater attention should be given to planning and goal setting related to microbial water risk communication.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.024 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".