Coping with eco-anxiety: An interdisciplinary perspective for collective learning and strategic communication
Bibliographic record
Abstract
Anthropogenic climate change and ecological crisis are affecting people's mental health. One such manifestation, eco-anxiety, is anxiety in the form of negative, troublesome, and automatic physiological, cognitive, emotional, and behavioral reactions to climate change and ecological degradation. The speed, scale, and severity of unfolding environmental crises will continue to exacerbate experiences of eco-anxiety. Scholars and practitioners are still in the early stages of understanding and addressing the phenomenon. To help prioritize future endeavors, we advocate for an interdisciplinary approach to address the urgency and complexity of eco-anxiety, which can be understood in the context of a larger problem facing humanity. We provide an eco-anxiety primer based on recent scoping reviews and seminal empirical research. Additionally, we recommend four opportunities for collective learning and strategic communication: (1) motivational and actionable message framing, (2) storytelling for social and behavior change, (3) knowledge sharing and linked resources, and (4) positive deviance for complex problem-solving. We hope this article will benefit health practitioners, media professionals, academic researchers, policy makers, community leaders, climate activists, and other stakeholders.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.038 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".