Compliance of bathers to showering before swimming in a public pool in Toronto, Ontario
Bibliographic record
Abstract
Bathers at public swimming pools should shower prior to entering the pool deck to remove organic material (e.g., sweat, lotions, fecal matter) that can increase the risk of recreational water illness and the formation of disinfection by-products. However, little research has been conducted to evaluate bathers’ pre-swimming showering practices. We conducted a cross-sectional study of bathers aged 18 years or older at a public swimming pool in Toronto, Ontario, to evaluate their showering habits. An in-person questionnaire was administered in October and November 2019. Bivariate associations were examined between selected variables and participants’ self-reported showering frequency prior to swimming (often or always vs. never, rarely, or sometimes). A total of 110 bathers agreed to participate. Most participants (63%) were aged 18–34, 56% identified as male, and 78.2% reported always or often showering before swimming. Of these individuals, only 34% reported using soap when showering. Participants that identified as male (vs. female) and an ethnicity other than white were more likely to report often or always showering, as were those that reported reading the pool rules and that observed other bathers taking a shower. Additional efforts are needed to educate bathers about the importance of showering prior to swimming in public pools.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".