It’s Hot Today, Eh? Montreal’s 2018 Heat Wave from Urgences-santé’s Perspective
Bibliographic record
Abstract
Introduction: A heatwave hit the Greater Montreal area during the week of July 1 to July 8. The Urgences-santé Corporation (USC), Quebec’s largest emergency medical service (EMS), saw its crews struggling to respond to a record-breaking number of emergency calls while going door-to-door to make sure the at-risk population was not overburdened by the heat. Aim: USC’s mission was to ensure its population’s emergency medical care and safety under extenuating conditions. In conjunction with our municipal partners and the public health services, we deployed an aggressive communication strategy, urging people to only call 911 in the case of a life-threatening emergency, with the aim of limiting ambulance transports. Methods: Ambulance resources were increased (> 20% compared to the same period in 2017). More than 60 media interviews were given. Paramedic supervisors were sent to emergency departments to contain the offload delays. USC’s community response team was going door-to-door in pre-identified urban heat islands (UHI), bringing medical attention directly to those in need. Results: Despite our communications efforts, a record-breaking 1,568 calls (> 37% compared to the same period in 2017) were received in a 24 hour period. Through the door-to-door campaign, 12 people in need received medical attention. More than 90 people are suspected to have died as a result of a July heat wave in Quebec, with figures showing that 60 deaths in the cities of Montreal and Laval alone may be linked to elevated temperatures. Discussion: Through strong collaboration with our municipal and provincial partners, and the public health services, an important communication strategy and additional resources were deployed. Crews were able to prevent additional deaths. With the observed increase in extreme weather events, this strategy will definitely be useful in the future.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.022 | 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".