Climate change and substance use disorders – do we understand the risks?
Bibliographic record
Abstract
Introduction Climate change is increasing the frequency and intensity of severe heatwaves, storms, floods, droughts, and wildfires. These events cause widespread economic and social disruption and are undermining population health worldwide. Despite a growing literature on how climate change threatens mental health, its influence on harmful substance use has not been systematically addressed. Objectives We propose an explanatory framework explicating the plausible links between climate change-related stressors and an increase in harmful substance use. Methods We critically review and synthesise literature documenting the pathways, processes and mechanisms linking climate change to increased substance use vulnerability. Results Several plausible pathways link climate change to increased risk of harmful substance use worldwide. These include: (1) anxiety about the impacts of unchecked climate change, (2) destabilisation of psychosocial and economic support systems, (3) increasing rates of mental disorders, and (4) increased physical health burden. Children may face disproportionate risk due to their vulnerability to both mental disorders and substance use, particularly during adolescence. We argue that a developmental life-course perspective situated within a broader ‘systems thinking’ approach provides a coherent framework for understanding how climate change is aggravating the multiple, persistent, interacting risks that influence harmful substance use pathways. Conclusions Climate change is already undermining health and wellbeing of global populations. By inference, it is also aggravating pathway to harmful substance use. This is a critical psychosocial problem for individuals and communities alike. Conceptual and methodological work is urgently needed so that effective adaptive and preventive action can be taken. Disclosure No significant relationships.
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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 | 0.001 | 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) | 0.000 | 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".