Pediatric climate distress: A scoping review and clinical resource
Bibliographic record
Abstract
Introduction: Climate change is the public health crisis of our time, with young people particularly at risk. Climate change has been associated with increased prevalence of psychiatric disorders. Psychological concerns pertaining to the Earth's future have also been cited as contributing to negative emotions now termed 'climate distress'. While previous reviews have addressed the various ways climate change affects pediatric mental health, this scoping review aims to specifically explore pediatric climate distress and its implications for clinical practice. Methods: 2548 articles were extracted from multiple databases, titles, abstracts, and full texts were screened blinded and in duplicate using the web-based platform Covidence. Quantitative and qualitative original research papers published in English between January 1, 2000 and April 29, 2024 that studied pediatric climate distress were included. Results: Forty-two articles met the inclusion criteria, along with 10 additional grey literature sources. Among quantitative studies, 81 % found that 50 % or more of respondents reported negative climate emotions and 86 % of qualitative studies reported that "all" or "most" respondents expressed negative climate emotions. Additionally, 63 % of studies measuring distress severity reported high distress levels. Therapeutic interventions addressing climate distress were found to be effective and were categorized thematically into three groups: Education-Emotion Focused, Nature-Engagement Based, and Activism-Civic Related. Conclusions: This review shows that while there is a growing body of literature that illustrates how young people have negative emotions pertaining to climate change, there is need for increased diagnostic and therapeutic approaches to clinically address these growing challenges.
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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 | 0.013 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.032 | 0.032 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".