Impacts of extreme temperatures on mood disorders: A systematic review
Bibliographic record
Abstract
BACKGROUND: Climate change has contributed to an increase in extreme temperatures globally, with mounting evidence suggesting a relationship between extreme temperature exposure and mental health. This review synthesizes findings on the impacts of extreme temperatures on several aspects of mood disorders. METHODS: This systematic review was conducted following the Preferred Reporting Items for Systematic reviews and Meta-Analyses guideline. Major databases (MEDLINE/PubMed, PsycINFO, Scopus, and Web of Science) were searched for eligible studies. Study quality was assessed using the Joanna Briggs Institute critical appraisal tool. RESULTS: = 2), encompassing diverse designs (case-crossover, cohort, and cross-sectional). High temperatures were linked to increased hospital admissions for mood disorders, especially among adolescents, women, and the elderly. Seventeen studies identified significant correlations between extreme heat and emergency department visits, whereas others reported minimal associations. Short-term exposure to humidity was a risk factor for increased mood disorder symptoms. Extreme cold exposure was associated with increased outpatient visits and heightened symptom severity for depressive disorders, particularly among older adults and females. The included studies were generally of moderate quality. CONCLUSIONS: The evidence from this review underscores the need for multi-pronged interventions, innovative practices, and public health strategies - including urban planning, patients' and public education, use of telemedicine, and policy measures - to mitigate the mental health consequences of climate change-driven extreme temperature events.
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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".