Mental health and climate change – a developmental life course perspective
Bibliographic record
Abstract
Introduction Climate change is a major global public health challenge that will have wide ranging effects on human psychological health and wellbeing through the increased incidence of acute (e.g., storms, floods, wildfires), sub-acute (e.g., heat stress, droughts, lost agricultural yields) and long-term stressors (e.g., changes to landscapes and ecosystems). Children and adolescents are particularly at risk because of their rapidly developing brain, vulnerability to disease and limited capacity to avoid or adapt to climate change-related threats and impacts. They are also more likely to worry about climate change impacts than any other age group. Objectives To produce a new conceptual framework that describes climate change-related threats to youth mental health from a developmental life course perspective. Methods We critically review and synthesis literature documenting the pathways, processes and mechanisms linking climate change to increased mental health vulnerability. Results We show that climate change-related threats can additively and interactively increase psychopathology risk from conception onwards, that these effects are already occurring and that they constitute an important threat to mental health and therefore human capital worldwide. We then argue that birth cohort studies are uniquely positioned to examine climate change-related threats and that incorporating relevant measures into existing and planned birth cohorts is a matter of social justice and crucial long-term investment in mental health research. Conclusions Climate change is affecting the healthy psychological development of children and these risks are increasing worldwide. New theoretical and empirical work is urgently needed so that threats can be tracked and mitigated.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".