Operational impacts at municipal aquatic facilities due to the COVID-19 public health measures
Bibliographic record
Abstract
Due to the COVID-19 pandemic, aquatic facilities were initially closed but then allowed to open with strict public health measures in place. However, it is unclear how these measures impacted pool operations. This study surveyed municipal pool operators in Ontario regarding what public health measures were adopted during the pandemic (March 2020 to March 2022). Results were reported using frequency descriptions and t-tests were conducted to compare responses during and after the pandemic. Overall, 48 pool operators participated, representing a response rate of 23.6%. Every operator made changes to pool entry procedures, implemented social distancing measures, posted public health notices and reduced swim class sizes. Some operators had issues hiring enough qualified staff and, in fact, had to reduce their hours or even close the facility. In addition, labour-intensive duties included increased frequency of cleaning and health screening of every pool visitor. The changes to pool operations and closures may have adversely impacted the mental health of staff and the public, respectively. In anticipation of another pandemic, it is recommended that pool operators provide training and support to their staff. Also, future pandemic guidelines ought to address both public health as well as occupational health and safety best practices.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".