Drinking Water Consumption Patterns among Private Well Users in Ontario: Implications for Exposure Assessment of Waterborne Infection
Bibliographic record
Abstract
Understanding the water consumption patterns within a specific population informs development of increasingly accurate, spatially specific exposure and/or risk assessment of waterborne infection. The current study examined the consumption patterns of private well users in Ontario while considering potentially influential underlying sociodemographics, household characteristics, and experiential factors. A province-wide online survey was circulated between May and August 2018 (n = 1,162). Overall, 81.5% of respondents reported daily well water consumption (i.e., tap water). Results indicate a mean daily well water consumption rate of 1,132 mL/day (SD = 649 mL/day) among well water consumers. Gender was significantly associated with well water consumption, with higher consumption rates found among female respondents. The experience of acute gastrointestinal illness (AGI) symptoms or diagnosis in the past 12 months did not impact the volume of water consumed, suggesting that experiencing previous AGI does not decrease consumption volumes, and therefore exposure over time. Significantly higher rates of well water consumption were found among respondents who reported previous testing or ongoing water treatment. Approximately 45.5% of survey respondents who stated that they do not consume well water selected bottled water as their primary household drinking water supply. Bottled water consumption was also not associated with previous AGI experiences. Findings will inform future quantitative microbial risk assessments associated with private well water use by providing spatially and demographically specific estimates of well water consumption.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".