Indoor overheating: A review of vulnerabilities, causes, and strategies to prevent adverse human health outcomes during extreme heat events
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The likelihood of exposure to overheated indoor environments is increasing as climate change is exacerbating the frequency and severity of hot weather and extreme heat events (EHE). Consequently, vulnerable populations will face serious health risks from indoor overheating. While the relationship between EHE and human health has been assessed in relation to outdoor temperature, indoor temperature patterns can vary markedly from those measured outside. This is because the built environment and building characteristics can act as an important modifier of indoor temperatures. In this narrative review, we examine the physiological and behavioral determinants that influence a person's susceptibility to indoor overheating. Further, we explore how the built environment, neighborhood-level factors, and building characteristics can impact exposure to excess heat and we overview how strategies to mitigate building overheating can help reduce heat-related mortality in heat-vulnerable occupants. Finally, we discuss the effectiveness of commonly recommended personal cooling strategies that aim to mitigate dangerous increases in physiological strain during exposure to high indoor temperatures during hot weather or an EHE. As global temperatures continue to rise, the need for a research agenda specifically directed at reducing the likelihood and impact of indoor overheating on human health is paramount. This includes conducting EHE simulation studies to support the development of consensus-based heat mitigation solutions and public health messaging that provides equitable protection to heat-vulnerable people exposed to high indoor temperatures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it