Susceptibility of persons with schizophrenia to extreme heat: A critical review of physiological, behavioural, and social factors
Bibliographic record
Abstract
Schizophrenia is a severe mental illness affecting approximately 24 million people worldwide. Schizophrenia diagnosis is associated with more than double the all-cause mortality risk of the general population and a 13- to 15-year reduction in life expectancy, due to a myriad of intersecting factors and underlying causes. Persons with schizophrenia are particularly susceptible to the negative health effects of extreme heat, as demonstrated by the 2021 Western North America Heat Dome. During this event, schizophrenia was associated with a 3-fold increase in the odds of mortality in British Columbia, Canada, far surpassing the risks associated with other common heat-sensitive chronic conditions, including diabetes, hypertension, and heart disease. While individual factors such as age, presence of chronic disease, and medication-use can impair thermoregulation and limit a person's ability to sense and respond to heat, physiological and behavioural factors associated with schizophrenia can exacerbate these impairments. In-turn, social and community-level factors play important roles in aggravating or mitigating heat-health risks. However, to date, our understanding of the separate and combined influence of the physiological, behavioural, and social determinants underpinning heat-susceptibility in individuals with schizophrenia remains largely unresolved. We therefore conducted a critical review to examine the physiological factors that can increase susceptibility to the negative effects of heat in persons with schizophrenia and profile the social and community-level factors aggravating or mitigating these risks. An interdisciplinary, multi-level approach is essential to facilitate effective heat-health planning and community adaptation to prevent heat-related injuries and deaths in persons with schizophrenia during extreme heat.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".