Determinants of bather hygiene in public swimming pools: a mixed-methods analysis of online discussion comments
Bibliographic record
Abstract
Swimming in public pools can expose bathers to microbiological (e.g., Cryptosporidium) and chemical (e.g., disinfection by-product) hazards, which can lead to recreational water illness. Adequate hygienic behaviours among bathers are important to reduce these risks. However, prior studies have found low compliance with pre-swim showering and avoidance of urinating in pools. We conducted a mixed-methods analysis of online discussion comments to identify key determinants of these two behaviours. We identified relevant discussion threads on Reddit, Quora, and swimming forums. Identified comments were classified as having a positive or negative sentiment towards the behaviour, and they were thematically analyzed according to the Theoretical Domains Framework (TDF). We analyzed 986 comments from 45 threads; 49.8% of comments were classified as expressing a positive sentiment towards the behaviour. Positive sentiments were more commonly expressed towards pre-swim showering compared to avoidance of urinating in pools (60.1 vs. 47.1%). Eleven themes were identified across the following eight TDF domains: beliefs about consequences (n = 362 comments), social influences (n = 298), beliefs about capabilities (n = 240), social/professional role and identity (n = 144), knowledge (n = 138), emotions (n = 137), reinforcement (n = 78), and environmental context and resources (n = 33). Results can help to inform targeted educational and outreach strategies with bathers to encourage increased adoption of hygienic behaviours.
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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 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".