Climate cafés as a space for navigating climate emotions: A scoping review
Bibliographic record
Abstract
Introduction: Climate change poses significant physical health risks, while its mental and emotional impacts are increasingly being recognized and researched. Although Climate Cafés have emerged as community-led interventions offering spaces to discuss climate-related thoughts and feelings, there is a paucity of literature describing their utility and impact. This scoping review maps the existing landscape of Climate Cafés and assesses their role in addressing climate-induced distress, and motivating action. Methods: A literature review was conducted using academic literature published between 2015 and 2024 from the MEDLINE, PsychINFO, Public Health Database, and Web of Science databases. A grey literature search was also undertaken to capture information not published in the academic literature. Results: No academic literature met inclusion criteria while the grey literature yielded 41 records. The grey literature depicted Climate Cafés as flexible, community-driven spaces for individuals to express and process emotions related to climate change. Programs varied: some were action-free spaces focused on emotional support while others promoted climate action. Participant feedback indicated reduced isolation, decreased anxiety, and increased hopefulness after attending sessions. Challenges identified include issues with inclusivity for marginalized communities, cultural barriers, logistical difficulties, and a lack of standardized evaluation methods. Conclusions: Climate Cafés represent a promising yet under-researched approach to addressing the emotional impacts of climate change. Further research is needed to evaluate the effectiveness of Climate Cafés, which could inform their integration into strategies to support both individual well-being and community resilience.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".