Public perceptions of drinking water: a postal survey of residents with private water supplies
Bibliographic record
Abstract
BACKGROUND: In Canada, the legal responsibility for the condition of private water supplies, including private wells and cisterns, rests with their owners. However, there are reports that Canadians test these water supplies intermittently and that treatment of such water is uncommon. An estimated 45% of all waterborne outbreaks in Canada involve non-municipal systems. An understanding of the perceptions and needs of Canadians served by private water supplies is essential, as it would enable public health professionals to better target public education and drinking water policy. The purpose of this study was to investigate the public perceptions of private water supplies in the City of Hamilton, Ontario (Canada), with the intent of informing public education and outreach strategies within the population. METHODS: A cross-sectional postal survey of 246 residences with private water supplies was conducted in May 2004. Questions pertained to the perceptions of water quality and alternative water sources, water testing behaviours and the self-identified need for further information. RESULTS: Private wells, cisterns or both, were the source of household water for 71%, 16% and 13% of respondents, respectively. Although respondents rated their water quality highly, 80% also had concerns with its safety. The most common concerns pertained to bacterial and chemical contamination of their water supply and its potential negative effect on health. Approximately 56% and 61% of respondents used in-home treatment devices and bottled water within their homes, respectively, mainly due to perceived improvements in the safety and aesthetic qualities compared to regular tap water. Testing of private water supplies was performed infrequently: 8% of respondents tested at a frequency that meets current provincial guidelines. Two-thirds of respondents wanted more information on various topics related to private water supplies. Flyers and newspapers were the two media reported most likely to be used. CONCLUSION: Although respondents rated their water quality highly, the majority had concerns regarding the water from their private supply, and the use of bottled water and water treatment devices was extensive. The results of this study suggest important lines of inquiry and provide support and input for public education programs, particularly those related to private water testing, 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".