Using indoor temperature in heat health warning systems: Deployment in community housing in Canada
Bibliographic record
Abstract
Abstract Background Heat waves are a major global public health concern and present a significant challenge to society, especially to underserved populations and those aged 50+, due to their increasing frequency and intensity. They also add significant burdens to the healthcare system's resources, and it has been shown that most deaths occur indoors. Still, our warning systems are based on outdoor temperature measurements. We developed a real-time indoor temperature alert ecosystem to capture indoor temperatures that may aid in assessing and responding during a heat wave. Methods We placed ecobee thermostats into homes with community housing partners and local health authorities (N = 70), recorded indoor temperature data in near real-time, and administered three surveys during the study period. We investigated: (i) indoor temperatures trends during the heatwave season of 2022 in Vancouver and Ottawa, Canada; (ii) behaviours of participants during the study; (iii) housing characteristics; (iv) delivery of time-sensitive temperature alerts to enable check-ins, and (v) perceptions to heat risk and methods of communication. Results Initial results show different observations that include: a) unsafe indoor temperatures were reached and persisted (>26 °C and 31 °C) despite a milder heat wave season (in British Columbia); b) gaps in understanding of best practices to stay safe exist; c) home characteristics increase risk of overheating; d) coordinating check-in can protect individual health; and e) ensure individuals are protected during extreme heat events. Results suggest that indoor temperature provides vital information to inform heat health response plans. Conclusions The smart thermostat technology can be retrofitted into homes in Canada as it is commercially available. This study presents a scalable solution for monitoring indoor temperatures; it demonstrates they can be used to monitor indoor temperature exposure conditions to prevent deaths during extreme heat events. Key messages • This study presents a scalable solution for monitoring indoor temperatures, demonstrating how smart thermostats can monitor indoor temperature conditions to prevent deaths during extreme heat events. • Indoor temperatures pose significant risks to health; this study seeks to understand indoor temperature exposures to guide public health resource allocation 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".