Living with Schizophrenia in a Changing Climate: Housing, Indoor Environmental Quality, and Health Risks
Bibliographic record
Abstract
The increasing frequency and intensity of heat waves is negatively impacting the health of individuals globally. Yet, there is a disparity in who heat harms. People with schizophrenia have died at disproportionately high rates in recent extreme heat events (EHE) in North America. This study will present the framing and approach to improved knowledge of how heat harms those with schizophrenia. We build on recent work that qualitatively explored the EHE experiences of individuals diagnosed with schizophrenia in British Columbia (BC), Canada, through which we found that this troubling trend reflects not only the physiological vulnerabilities associated with schizophrenia but also the systemic inequities in housing, social support, and access to care that compound their risk. To supplement these invaluable qualitative accounts, we aim to better understand heat experiences of individuals diagnosed with schizophrenia by considering the intersection of indoor environmental quality (IEQ), physical and mental health data (cognition, mood), and the housing security conditions that affect thermal comfort and safety. Together with clinicians, geographers and community partners, our interdisciplinary study that will a) monitor Indoor Environmental Quality (IEQ) - using environmental sensors to monitor indoor temperature, humidity, and air quality over two summer seasons, assessing how these factors contribute to heat stress and overall well-being; b) assess Health and Mood Impacts via biannual questionnaires, health data from wearable fitness trackers, and cognition and mood assessments to explore how extreme and chronic heat, combined with poor air quality, influence the physical and mental health of people with schizophrenia; and d) amplify participant voices through Photovoice, where participants will document their experiences of extreme heat and poor air quality through photography and personal narratives. This humanizes the impacts of climate change on vulnerable populations and provides rich, qualitative insights into the ways structural inequities shape vulnerability to environmental hazards.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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 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".