MétaCan
Menu
Back to cohort
Record W2511521620 · doi:10.5864/d2016-019

An exploratory study of the implementation of admission standards (child:guardian ratios) in Ontario's Class A public pools

2016· article· en· W2511521620 on OpenAlexaffvenueabout
Ofelia Tatar, Chun‐Yip Hon

Bibliographic record

VenueEnvironmental Health Review · 2016
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsGuardianRecreationPublic healthExploratory researchChristian ministryChild careEnvironmental healthMedicineBusinessNursingPolitical science

Abstract

fetched live from OpenAlex

Admission standards or specific child:guardian ratios for public pools have been endorsed and promoted by the Ontario Ministry of Health and Long Term Care (MOHLTC) to prevent recreational water injuries and fatalities. However, the voluntary adoption of these admission standards in Ontario has not been evaluated. Therefore, the aim of this study was to explore the implementation of these admission standards in Class A public pools. An online survey was developed and disseminated to Class A public pool operators in Ontario. Frequency distributions were used to describe the results. All respondents have some form of admission standards integrated into their operations, with 68% using child:parent ratios that exceed the minimum MOHLTC's recommendations. The majority of operators (87%) felt that admission standards have a positive impact and there were no known increases in water-related incidents post-implementation. Many owners/operators (78%) would support their enactment into the pool regulations. The findings from this study highlight the promise of utilizing admission standards to prevent or, at the least reduce, the burden of injury related to recreational water use in Ontario. While the results are encouraging, it is recommended that further research be conducted as this was an exploratory study only.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.045
GPT teacher head0.399
Teacher spread0.354 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes3
Has abstractyes

Explore more

Same venueEnvironmental Health ReviewSame topicInjury Epidemiology and PreventionFrench-language works237,207