Climate change is impacting mental health in North America: A systematic scoping review of the hazards, exposures, vulnerabilities, risks and responses
Bibliographic record
Abstract
As climate change progresses, it is crucial that researchers and policymakers understand the ways in which climate-mental health risks arise through interactions between climate hazards, human exposure and social vulnerabilities across time and location. This scoping review systematically examined the nature, range and extent of published research in North America that investigates climate-mental health interactions. Five electronic databases were searched and two independent reviewers applied pre-determined criteria to assess the eligibility of articles identified in the search. Eighty-nine articles were determined to be relevant and underwent data extraction and analysis. The published literature reported on numerous exposure pathways through which acute and chronic climate hazards interacted with social vulnerabilities to increase mental health risks, including wellbeing, trauma, anxiety, depression, suicide and substance use. This review also highlights important gaps within the North American climate-mental health evidence base, including minimal research conducted in Mexico, as well as a lack of studies investigating climate-mental health adaptation strategies and projected future mental health risks. Further research should support effective preparation for and adaptation to the current and future mental health impacts of climate change. Such strategies could reduce health risks and the long-term mental health impacts that individuals and communities experience in a changing climate.
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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".