Developing a harmonized heat warning and information system for Ontario: a case study in collaboration
Bibliographic record
Abstract
BACKGROUND: Heat wave early warning systems help alert decision-makers and the public to prepare for hot weather and implement preventive actions to protect health. Prior to harmonization, public health units across Ontario either used independent systems with varying methodologies for triggering and issuing public heat warnings or did not use any system. The federal government also issued heat warnings based on different criteria. During heat events, adjacent public health units in Ontario and the federal government would routinely call heat warnings at different times with separate public messages, leading to confusion. This article describes the collaborative process and key steps in developing a harmonized Heat Warning and Information System (HWIS) for Ontario. SETTING: Public health units across Ontario, Canada, collaborated with the federal and provincial government to develop the harmonized HWIS for Ontario. INTERVENTION: In 2011, stakeholders identified the need to develop a harmonized system across Ontario to improve heat warning services, warning criteria, and health messaging. Through a 5-year process facilitated by a non-governmental organization, the three levels of government collaborated to establish the Ontario HWIS. OUTCOMES: The province-wide HWIS was implemented in 2016 with the Ontario Ministry of Health and Long-Term Care's release of the harmonized HWIS Standard Operating Practice, which outlined the notification and warning process. IMPLICATIONS: The lessons learned could help spur action in other provinces and jurisdictions internationally in the development of similar health evidence-based warning systems, including in particular those for protecting public health during extreme heat events.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.021 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".