Bibliographic record
Abstract
Adequate access to safe drinking water is important in maintaining public health. Over 400,000 rural Albertans use well water for domestic purposes. The current policy on the management of private water wells requires well owners be responsible for their own water well management and well water quality. Therefore, the decision of when to test well water, what to test for, and what treatments to use to safeguard or improve water quality, lies with well owners. The purpose of this thesis was to 1. Describe the perceptions, knowledge, and beliefs rural Albertan residents have of well water quality and whether they associate livestock farming with water well contamination. 2. Identify the barriers faced by water well owners with respect to implementing well water stewardship practices. 3. Identify factors associated with water well stewardship practices (i.e., testing and treatment). A mixed methods study was completed which included a systematic review, interviews with well owners, a questionnaire survey of well owners, and collection of well water samples to assess for microbiological indicators of drinking water contamination. Thematic analyses were used to understand factors shaping perceptions of well water quality and identify factors influencing water testing behaviour as viewed through the lens of the Health Belief Model. Descriptive statistics and logistic regression analyses were used to understand the characteristics of well owners, well use, well stewardship practices, as well as investigate associations between independent variables and well stewardship practices. Barriers to treatment included a lack of awareness of what treatments to use. Increased education and awareness may be important to increase the adoption of well stewardship practices. Several factors were found to influence perceptions of well water quality. Furthermore, well owners described issues such as low perceived susceptibility to water well contamination and logistical barriers when submitting water samples for testing.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".