Factors influencing perceptions of private water quality in North America: a systematic review
Bibliographic record
Abstract
BACKGROUND: An estimated four million and 43 million people in Canada and the USA use private water supplies. Private water supplies are vulnerable to waterborne disease outbreaks. Private water supplies in Canada and the USA are often unregulated and private water management is often a choice left to the owner. Perceptions of water quality become important in influencing the adoption of private water stewardship practices, therefore safeguarding public health. METHODS: We conducted a systematic literature review to understand factors that shape perceptions of water quality among private water users. We searched six computer databases (Web of science, Medline, Scopus, EBSCO, PubMed and Agricola). The search was limited to primary peer-reviewed publications, grey literature and excluded conference proceedings, review articles, and non-peer review articles. We restricted the search to papers published in English and to articles which published data on surveys of private water users within Canada and the USA. The search was also restricted to publications from 1986 to 2017. The literature search generated 36,478 records. Two hundred and four full text were reviewed. RESULTS: Fifty-two articles were included in the final review. Several factors were found to influence perceptions of water quality including organoleptic preferences, chemical and microbiological contaminants, perceived risks, water well infrastructure, past experience with water quality, external information, demographics, in addition to the values, attitudes, and beliefs held by well owners. CONCLUSIONS: Understanding the factors that shape perceptions of water quality among private water users is an important step in developing private water management policies to increase compliance towards water testing and treatment in Canada and the USA. As many jurisdictions in Canada and the USA do not have mandatory private water testing or treatment guidelines, delineating these factors is an important step in informing future research and guiding policy on the public health of private water systems.
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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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".