Investigating the Mental Health Impacts of Climate Change in Youth: Design and Implementation of the International Changing Worlds Study
Bibliographic record
Abstract
As climate change continues unabated, research is increasingly focused on capturing and quantifying the lesser-known psychological responses and mental health implications of this humanitarian and environmental crisis. There has been a particular interest in the experiences of young people, who are more vulnerable for a range of reasons, including their developmental stage, the high rates of mental health conditions among this population, and their relative lack of agency to address climate threats. The different geographic and sociocultural settings in which people are coming of age afford certain opportunities and present distinct challenges and exposures to climate hazards. Understanding the diversity of lived experiences is vitally important for informing evidence-based, locally led psychosocial support and social and climate policies. In this Project Report we describe the design and implementation of the “Changing Worlds” study, focusing on our experiences and personal reflections as a transdisciplinary collaboration representing the UK, India, Trinidad and Tobago, Guyana, Barbados, the Philippines, and the USA. The project was conceived within the planetary health paradigm, aimed at characterizing and quantifying the impacts of human-mediated environmental systems changes on youth mental health and wellbeing. With input from local youth representatives, we designed and delivered a series of locally adapted surveys asking young people about their mental health and wellbeing, as well as their thoughts, emotions, and perceived agency in relation to the climate crisis and the global COVID-19 pandemic. This project report outlines the principles that guided the study design and describes the conceptual and practical hurdles we navigated as a distributed and interdisciplinary research collaboration working in different institutional, social, and research governance settings. Finally, we highlight lessons learned, specify our recommendations for other collaborative research projects in this space, and touch upon the next steps for our work. This project explicitly balances context sensitivity and the need for quantitative, globally comparable data on how youth are responding to and coping with environmental change, inspiring a new vision for a global community of practice on mental health in climate change.
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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 | 0.057 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".