Canadian beach cohort study: protocol of a prospective study to assess the burden of recreational water illness
Bibliographic record
Abstract
BACKGROUND: Recreational water activities at beaches are popular among Canadians. However, these activities can increase the risk of recreational water illnesses (RWI) among beachgoers. Few studies have been conducted in Canada to determine the risk of these illnesses. This protocol describes the methodology for a study to determine the risk and burden of RWI due to exposure to fecal pollution at beaches in Canada. METHODS: This study will use a mixed-methods approach, consisting of a prospective cohort study of beachgoers with embedded qualitative research. The cohort study involves recruiting and enrolling participants at public beaches across Canada, ascertaining their water and sand contact exposure status, then following-up after seven days to determine the incidence of acute RWI outcomes. We will test beach water samples each recruitment day for culture-based E. coli, enterococci using rapid molecular methods, and microbial source tracking biomarkers. The study started in 2023 and will continue to 2025 at beaches in British Columbia, Manitoba, Ontario, and Nova Scotia. The target enrollment is 5000 beachgoers. Multilevel logistic regression models will be fitted to examine the relationships between water and sand contact and RWI among beachgoers. We will also examine differences in risks by beachgoer age, gender, and beach location and the influence of fecal indicator bacteria and other water quality parameters on these relationships. Sensitivity analyses will be conducted to examine the impact of various alternative exposure and outcome definitions on these associations. The qualitative research phase will include focus groups with beachgoers and key informant interviews to provide additional contextual insights into the study findings. The study will use an integrated knowledge translation approach. DISCUSSION: Initial implementation of the study at two Toronto, Ontario, beaches in 2023 confirmed that recruitment is feasible and that a high completion rate (80%) can be achieved for the follow-up survey. While recall bias could be a concern for the self-reported RWI outcomes, we will examine the impact of this bias in a negative control analysis. Study findings will inform future recreational water quality guidelines, policies, and risk communication strategies in Canada.
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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 | 0.019 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.007 |
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".