Meaning making and fostering radical hope: applying positive psychology to eco-anxiety research in youth
Bibliographic record
Abstract
The consequences of human activity on climate change are increasingly apparent. For example, they are causing ecological degradation and affecting human and animal health. Rightly so, it is considered as the most important challenge of this century. Researchers in psychology and mental health developed an interest in the direct and indirect effects of climate and ecological change on people's psychological wellbeing, which is referred to as a concept described as eco-anxiety or eco-distress. It is worth emphasizing that climate issues are taking a larger place in the school curriculum for youth in elementary, middle and high schools. Youth are thus increasingly aware of the major threat and understandably report legitimate concerns and worries. For some youth, eco-anxiety leads to greater involvement and activism, as can be seen by the international movement set out and led by youth activist Greta Thunberg. However, eco-anxiety can also lead to feelings of hopelessness and disengagement. Despite contributing the least to the climate and ecological crises, youth will be most affected by the impacts, and will carry the burden of the climate crisis throughout their lives. Researchers, educators and mental health professionals must therefore find ways to foster youth psychosocial wellbeing and resilience alongside ensuring that their voices are heard. To this end, it is vital that young people feel able to openly discuss climate change and associated issues alongside the distressing thoughts and feelings they engender. This can be supported by using various psychological approaches to develop effective interventions. Researchers and clinicians in child mental health could gain from drawing from research in positive psychology to develop such interventions. In this review and commentary, we will outline how eco-anxiety and child psychological wellbeing can be framed within a positive psychology framework, including the relevance of self-determined motivation. Insights from interventions based on positive psychology including exercises to foster hope, forgiveness and meaning making will also be discussed. We will highlight how such interventions can be adapted as powerful tools to foster child wellbeing and cope with their eco-anxiety.
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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 | 0.016 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.008 |
| 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".