An examination of opportunities for small non-community drinking water systems to improve drinking water safety
Bibliographic record
Abstract
Waterborne diseases are among the world’s most significant yet largely preventable public health issues. A large number of waterborne disease outbreaks in developed countries are attributed to water from small, non-municipal water systems (SDWSs). This thesis is an investigation of the challenges and opportunities to improve the safety of the water supply in SDWSs, with a focus on systems located in Ontario. This thesis utilized data from a systematic review, SDWS data from Ontario, a cross sectional survey of SDWS operators and focus groups of public health inspectors. These data were used to (1) summarize factors contributing to SDWS outbreaks (2) investigate the relationships between certain key characteristics of SDWSs and the performance of these systems, and (3) explore the experience and future training needs of SWDS operators and public health inspectors in Ontario. This research found the leading causes of outbreaks in SDWSs were the failure of an existing water treatment system (22.7%) and lack of water treatment (20.2%). In Ontario, 66% of water operators were not trained and 16% had one year or less experience. Thirty four percent of systems utilized water treatment and 45% operated on a seasonal basis. The odds of having a positive E. coli test result were greater in systems using ground and surface water with treatment compared to ground water with no treatment (apparently indicating the failure of an existing water treatment system). Operators had a preference for online training courses or on-site training. SDWS inspectors reported needing support in the form of education initiatives and a community of practice to promote knowledge exchange. Main concerns to water safety were the technical ability of the water operator and having a long time period between inspections of water systems. \nThe findings of this research help identify opportunities to target training to specific groups of operators. There is a need to further explore the effectiveness of water treatment and other water protection measures currently in place in SDWSs in Ontario. Future research is needed to explore the cost-benefit of increasing inspection frequency and to explore a variety of education initiatives for inspectors and operators of SDWSs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".