“This is what I love and this is what’s at risk”: how climate grief reveals values that inspire climate action
Bibliographic record
Abstract
Climate grief, including pain and sadness related to climate change and its impacts on life and society, is increasingly recognized in global discourse about climate change and mental health. However, research on coping practices that support well-being and/or galvanize climate action remains limited. This study characterized how recognizing and honoring climate grief improved well-being and connected participants’ to values that motivate climate action. Semi-structured, in-depth interviews were conducted with adults (n =15) who had attended group-based climate-mental-health programs by Refugia Retreats in Alberta, Canada focused on climate grief. Reflexive thematic analysis was used to develop themes from the interviews. Findings show that climate grief connected personal experiences of loss and vulnerability to broader social-ecological issues by emphasizing the impact of climate change on ecological, personal, and collective losses. Interviewees also described a multi-directional relationship between climate grief and positive emotions, wherein attending to grief led to positive emotions, and in some cases positive experiences were accompanied by grief. Interviewees emphasized the value of reframing distressing climate emotions as grief because doing so connected them to love for the world and their desire for positive change. This study deepens our understanding of the psychosocial impacts of climate change and highlights the mobilizing potential of climate grief to connect people with personal values that can inspire climate action.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".