Do neuroticism and efficacy beliefs moderate the relationship between climate change worry and mental wellbeing?
Bibliographic record
Abstract
BACKGROUND: Research on the nature and prevalence of phenomena like climate anxiety (or eco-anxiety) is increasing rapidly but there is little understanding of the conditions under which climate change worry becomes more or less likely to significantly impact mental wellbeing. Here, we considered two plausible moderators of the relationship between climate change worry and mental wellbeing: neuroticism and efficacy beliefs. METHODS: Analysis was conducted with survey data gathered in six European countries in autumn 2019. Participants were recruited from universities in the participating countries using opportunity sampling. RESULTS: We found that climate change worry is negatively related to mental wellbeing at any level of perceived efficacy. In contrast, climate change worry is only significantly related to mental wellbeing at low and average levels of neuroticism. High neuroticism appears to have a masking, rather than amplifying, role in the relationship between climate change worry and mental wellbeing. LIMITATIONS: The cross-sectional design of the study precludes verification of causal relationships among variables. The brief measure of neuroticism employed also did not allow for nuanced analysis of how different facets of neuroticism contribute to the observed interaction with climate change worry. Findings cannot be indiscriminately generalised to less privileged groups facing the worst impacts of the climate crisis. CONCLUSION: Our findings lend to a view that harmful impacts of climate change worry on mental wellbeing cannot simply be ascribed to dispositional traits like neuroticism. We advocate for interventions that tackle negative climate-related emotions as unique psychological stressors.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".