Exploring Well Water Testing Behaviour Through the Health Belief Model
Bibliographic record
Abstract
Health problems can arise from consuming contaminated well water. Well water testing can help prevent negative health outcomes associated with consuming contaminated water. The aim of this study was to understand the factors influencing well owner decisions to conduct water testing through the theoretical lens of the Health Belief Model. We conducted semi-structured interviews with 20 well owners and used framework analysis to sort and analyse the data. The results demonstrated that well owners' perceived susceptibility to well water contamination was low, while the perceived severity of contamination, benefits of testing, and self-efficacy towards testing were high. Cues to action to promote testing focused on increasing well owner education and awareness through well stewardship programmes and reminders to test. Participants faced some barriers to water testing. Increasing education and awareness about well water contamination and water testing, while reducing logistical barriers to testing, may improve compliance with water testing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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 teacher head, 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".