Global priorities for climate change and mental health research
Bibliographic record
Abstract
BACKGROUND: Compared with other health areas, the mental health impacts of climate change have received less research attention. The literature on climate change and mental health is growing rapidly but is characterised by several limitations and research gaps. In a field where the need for designing evidence-based adaptation strategies is urgent, and research gaps are vast, implementing a broad, all-encompassing research agenda will require some strategic focus. METHODS: We followed a structured approach to prioritise future climate change and mental health research. We consulted with experts working across mental health and climate change, both within and outside of research and working in high, middle, and low-income countries, to garner consensus about the future research priorities for mental health and climate change. Experts were identified based on whether they had published work on climate change and mental health, worked in governmental and non-governmental organisations on climate change and mental health, and from the professional networks of the authors who have been active in the mental health and climate change space. RESULTS: Twenty-two experts participated from across low- and middle-income countries (n = 4) and high-income countries (n = 18). Our process identified ten key priorities for progressing research on mental health and climate change. CONCLUSION: While climate change is considered the biggest threat to global mental health in the coming century, tackling this threat could be the most significant opportunity to shape our mental health for centuries to come because of health co-benefits of transitioning to more sustainable ways of living. Research on the impacts of climate change on mental health and mental health-related systems will assist decision-makers to develop robust evidence-based mitigation and adaptation policies and plans with the potential for broad benefits to society and the environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".