Exploring Well Water Stewardship in Rural Ontario Communities Via a Mixed-Methods Approach: Implications For Drinking Water Consumers And Public Health Risks
Bibliographic record
Abstract
Private groundwater wells represent the primary source of household drinking water for at least 1.6 million individuals in the province of Ontario. In contrast to those who consume water from public (municipal) systems, private well users in Ontario are responsible for well water stewardship (i.e., protective actions), including source maintenance, treatment, and submitting samples for contaminant testing. Previous studies have reported low participation rates with respect to these actions, thus constituting a public health concern (e.g., contamination from agricultural run-off, septic tanks, etc.). The current thesis sought to classify private well owners in Ontario based on socio-cognitive factors, explore, and evaluate relationships between socio-cognitive factors (i.e., awareness, attitudes, perceptions, beliefs) and behaviours, and contextualize relationships between motivators and barriers to undertaking protective actions. A mixed-methods approach was employed, including a province-wide online survey (May to August 2018) and semi-structured interviews in two rural communities in southeastern Ontario (i.e., Town of Greater Napanee and Stone Mills Township) (March to June 2021). Survey data were used to quantify well owners’ awareness, perceptions, and behaviours, with two-step cluster analysis and binary logistic regression used to classify and profile the survey cohort based on cognitive factors. Thematic analysis of data from subsequent semi-structured interviews was employed to further elucidate initial quantitative results. Overall, 1140 survey respondents were included for quantitative analyses, and 40 semi-structured interviews (20 per community) were included for qualitative analyses. Survey findings illustrate that specific socio-cognitive and socio-demographic factors are significantly associated with user behaviours. Additionally, three distinct respondent clusters (i.e., socio-cognitive profiles) were identified based on two socio-cognitive factors (i.e., awareness and risk perception). Qualitative findings suggest complacency around well water quality and lack of risk perception are the main barriers to routine testing and/or treatment. Both quantitative and qualitative findings demonstrate that specific socio-cognitive and experiential factors are potential drivers of, and barriers to, undertaking protective actions, highlighting the need for tailored knowledge-translation initiatives and evidence-based tools. Results have crucial implications for risk communication and may be used to guide development of future public health interventions and stewardship tools to enhance protective actions among private well owners in Ontario and further afield.
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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 | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".