Use of a health information telephone line, Info-Santé CLSC, for the surveillance of waterborne gastroenteritis
Bibliographic record
Abstract
The increasing frequency of waterborne outbreaks demonstrates that classic indicators used for the surveillance of the microbiological quality of drinking water have several gaps and that routine public health surveillance seems insufficient to allow for the rapid detection of these outbreaks. The main objective of this study was to evaluate the possibility of using a regional health information telephone line, 'Info-Santé CLSC' (Info-Health Local Community Health Centre), for the surveillance of waterborne gastroenteritis. This study measured the incidence rate of calls for acute gastrointestinal illness (AGI) placed to the Info-Santé CLSC line, investigated the relationship between the frequency of calls for AGI placed to the Info-Santé CLSC line and the turbidity of the treated water in the Quebec City drinking water plant and evaluated the relevance and the conditions of use of the Info-Santé CLSC system for the surveillance of waterborne enteric illness. A relationship between the turbidity and the calls for AGI placed to Info-Santé CLSC line was observed. Significant time lags (11, 15 and 17 days prior to the outcome) were identified in the final model derived from a Poisson model using generalized additive models (GAM) as a time series analysis. Some recommendations to improve the system were formulated even though the system already seems to be useful for the surveillance of waterborne enteric diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".