Quantitative methods for climate change and mental health research: current trends and future directions
Bibliographic record
Abstract
The quantitative literature on climate change and mental health is growing rapidly. However, the methodological quality of the evidence is heterogeneous, and there is scope for methodological improvement and innovation. The first section of this Personal View provides a snapshot of current methodological trends and issues in the quantitative literature on climate change and mental health, drawing on literature collected through a previous scoping review. The second part of this Personal View outlines opportunities for methodological innovation concerning the assessment of the relationship between climate change and mental health. We then highlight possible methodological innovations in intervention research and in the measurement of climate change and mental health-related variables. This section draws upon methods from public mental health, environmental epidemiology, and other fields. The objective is not to provide a detailed description of different methodological techniques, but rather to highlight opportunities to use diverse methods, collaborate across disciplines, and inspire methodological innovation. The reader will be referred to practical guidance on different methods when available. We hope this Personal View will constitute a roadmap and launching pad for methodological innovation for researchers interested in investigating a rapidly growing area of research.
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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 | 0.249 | 0.319 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.014 | 0.022 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".