Bibliographic record
Abstract
This paper provides an overview of the current and prospective climate change-related risks and impacts on individual and community mental health, as outlined by studies from the United States, Canada, the United Kingdom, Australia, Mexico, and Pakistan, in addition to a few European countries from 2000 to January 2022. It argues three major points, first, certain vulnerabilities exist with regards to which populations are most atrisk of experiencing poor psychological well-being. The main vulnerabilities and risk factors highlighted in the paper are low socioeconomic background, young age, and communities having close cultural and working relationships with the environment. Second, climate change-induced natural disasters such as floods, hurricanes, wildfires, and heatwaves can have several impacts on mental health, mainly due to worsening physical health, disruption of community cohesion, and forced relocation. The concept of community resilience is also discussed. Finally, the relationship between heat waves and increased psychological fatigue and feelings of hostility is also explored, linked with rising crime rate which can further impact individual and community mental health. It was concluded that climate change impacts individual and community mental health in many ways and that certain gaps in knowledge, such as the factors influencing the severity of this impact and the reasons behind the existence of vulnerabilities among populations, need to be addressed and incorporated into future action. Moreover, adaptive action needs to be taken in preparing societies for the impact of climate change. This includes increasing accessibility to quality mental healthcare and creating protective legal frameworks for those who are disproportionately affected by interpersonal violence during and after climate-related natural disasters.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".