Characteristics of small residential and commercial water systems that influence their likelihood of being on drinking water advisories in rural British Columbia, Canada: a cross-sectional study using administrative data
Bibliographic record
Abstract
Health officials often lack information about characteristics that predict which water systems are most likely to be placed on and to persist on drinking water advisories (e.g. health warnings offering advice or information). This study uses data collected by the Interior Health Authority in British Columbia to characterize water systems on advisory for microbiological threats and to identify the variables associated with advisory status and length. By systematically extracting key characteristics, this study explores advisory status by examining associated variables: water systems size, administrative area, governance structure, water source, treatment level, and service type (e.g. residential or commercial systems). Results show residential and commercial water systems have different characteristics associated with advisory status and length. For residential systems, certain governance structures are more likely to be placed on and to stay on advisory, especially the cooperative governance structures not operated by local governments. For commercial systems, administrative area and system size were associated with advisory status, but not advisory length. The overall results highlight the influence of governance structure and support the need for targeted interventions to improve residential small water systems not operated by local governments or utilities. Lastly, these results show how health officials can use administrative data for program planning and evaluation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".