The impact of climate change-related disasters on mental health and epigenetics: a narrative review
Bibliographic record
Abstract
Climate change has contributed to an increase in the frequency and intensity of natural disasters such as droughts, wildfires, hurricanes, and floods, leading to both immediate physical harm and long-term mental health consequences. Survivors often experience psychological distress, including anxiety, depression, and post-traumatic stress disorder (PTSD), as a result of these traumatic events. This narrative review explores the intersection of climate change-related disasters, mental health outcomes, and epigenetic modifications. Specifically, we summarize recent findings on how environmental stressors influence both mental health and epigenetic changes, such as DNA methylation. Emerging evidence suggests that epigenetic mechanisms, particularly DNA methylation, could mediate the effects of climate change-related stress on mental health, potentially contributing to the onset of mental disorders like depression, panic disorder, autism spectrum disorder, and attention deficit hyperactivity disorder. We also discuss other epigenetic mechanisms, such as histone modifications and non-coding RNAs, and emphasize the importance of longitudinal studies to capture the dynamic nature of epigenetic changes over time. Finally, we propose future research directions to deepen our understanding of the complex relationships between climate change-related disasters, mental health outcomes, and epigenetic mechanisms, which will pave the way for more effective mental health interventions and policy integration.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".