Review of Canadian Legislation on Mass Gathering Medical Response
Bibliographic record
Abstract
Introduction: Mass gatherings have become more frequent since the beginning of the 21st century. In Canada alone, music festival and sporting event industries will each represent yearly revenues over one billion USD by 2025. Such events require adequate medical planning, as they are associated with a greater prevalence of injuries and incidents than daily life, despite most participants having few comorbidities. Most often, the responsibility of medical planning lies with event producers. This study aims to compare the existing legislative requirements for mass gathering medical response in the ten provinces and three territories of Canada. Method: This study is a cross-sectional descriptive study of legislation. Lists of legislative requirements were obtained by contacting via email or phone the emergency medical services (EMS) directors and Health Ministries of all the provinces and territories of Canada, and asking about any legislation or provision within existing laws regarding mass gatherings. Simple statistics were performed to compare legislation across provinces and territories. Results: Data collection and analysis are planned to be completed by December 31, 2022. Initial data collection and analysis revealed that none of the seven provinces who answered our emails have provincial legislations. Two referred to specific provisions in the Public Health laws of their province, though nothing specifically refers to mass gatherings. One confirmed that mass gathering medical response was a municipal/local concern to be addressed by the event producers and the locality where the event takes place, and one referred to guidelines published in 2014. Conclusion: Although some provinces and territories referred to provisions contained in public health legislation, none of the provinces reached to date could list specific legislation on mass gathering medical response. If this trend continues through full data analysis, it will highlight once more the need to provide more standardized guidance to organizers and municipalities in planning medical response.
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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 | 0.026 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.024 | 0.033 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".