A qualitative exploration of the public perception of municipal drinking water
Bibliographic record
Abstract
Surveys of water consumption patterns in Canada and the USA show a high frequency of alternative water use, including bottled water and water treatment devices. An in-depth understanding of the public perception of municipal water would enable public health and water utility professionals better to address the needs of residents in their jurisdictions. We explored these perceptions and the self-described behaviour and needs of participants served by municipal water systems in the City of Hamilton, Ontario (Canada). We conducted three focus groups; two with men and women aged between 36 and 65 years, and one with men and women aged between 20 and 35 years. In general, participants expressed negative views of the municipal water supplied to their homes. Concerns included unpleasant sensory qualities of the water, perceived poor source-water protection, a perceived insufficiency in water treatment and testing and potential contamination along the distribution system. Reasons for alternative water use included perceived improvements in quality and safety over regular tap water, although convenience also contributed to bottled water use. Participants wanted more information on water testing and suggested a variety of dissemination approaches. This study suggested important lines of inquiry and action regarding the perception of municipal drinking water in this population.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".