Increasing Access to Cooling in the Community: A Trial Heat Relief Network for Toronto
Bibliographic record
Abstract
Toronto Public Health (TPH) estimates that extreme heat contributes to an average of 120 excess deaths per year in Toronto. Very hot weather or extended periods of heat can increase health impacts among all people.Toronto Public Health coordinates the Hot Weather Response Plan under which key partners undertake activities aimed at reducing heat-related illness, including opening seven cooling centres when either a Heat Warning or Extended Heat Warning is in effect. Cooling Centres are designated air-conditioned locations that provide a place for people and their pets to escape the heat, rest, drink water, have a light snack and get information on how to prevent heat-related health impacts.To inform how Toronto can best offer cooling to vulnerable residents, in 2017, TPH conducted a jurisdictional scan of heat relief programs across North America and identified common features of heat relief facilities, including the following factors that make them more effective: 1) maximizing the number, and broadening the definition of facilities that are designated as heat relief facilities; 2) selecting facilities where people already choose to spend their time; and 3) allocating resources to promote and coordinate work. Based on the results of this scan, TPH will be promoting a Heat Relief Network that incorporates nearly 200 air conditioned libraries and community centres for the 2018 heat season.In addition, the Heat Relief Network will attempt to overcome barriers that prevent residents from using the existing Cooling Centre program including: limited access to transportation, fear or inability to leave home, stigma and reluctance to spend time in a place without activities.This presentation will focus on the population needs, program design, resident engagement and challenges encountered while implementing the Heat Relief Network.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".