Assessing the Geographic and Socioeconomic Determinants of Private Well Water Testing Practices in Southern Ontario
Bibliographic record
Abstract
Purpose: An estimated 4.3 million Canadians use private wells for daily water consumption, however well testing rates are declining, potentially resulting in an increased risk of exposure to groundwater contaminants associated with acute gastrointestinal infections (AGIs). Regular testing of well water is recommended to reduce the risk of consuming contaminated groundwater. Methods: This study used a large dataset composed of well testing and well construction data maintained by the government of Ontario. All tests conducted in southern Ontario between 2012 and 2016 were included. Log-binomial regression was used to investigate the association between SES and well water testing practices, with season and index test status included as covariates. Rurality, based on population density, was assessed as an effect modifier of this relationship. Results: The dataset contained information for 417,406 individual wells, 114,820 (27.51%) of which were tested during the study period, with two thirds (66.72%) of these sampled more than once. In urban (>400 people/km2) and peri-urban regions (>150 and <400 people/km2), wells located in low socioeconomic status (SES) areas were 14% and 15% less likely to be tested compared to high SES areas (RR: 0.86 (0.78, 0.95) and RR: 0.85 (0.76, 0.94), respectively). In rural regions (<150 people/km2), wells located in low SES areas were 13% more likely to be tested compared to high SES areas (RR: 1.13 (1.11, 1.15)). SES was not significantly associated with repeat testing in urban/peri-urban regions and was weakly associated in rural regions (RR = 1.06 (1.04, 1.07)). Positive index tests were associated with a 17% increased likelihood of repeat testing when compared to negative index tests, while accounting for the effect of season and SES (RR = 1.17 (1.16, 1.18)) Conclusion: Rurality and SES are important predictors of the decision to test a well, with index test status the most influential predictor of repeated well testing. Further research is required to assess the influence of SES at an individual level. These findings provide important information for public health agencies in the context of strategic and targeted water testing promotion.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".