Exploring the Relationship between Subjective Social Disconnectedness and Climate Change Anxiety
Bibliographic record
Abstract
Climate change is contributing to mental health challenges globally and there is a need to identify pathways that can mitigate these effects. Relational factors that are linked with higher resilience and improved mental health are understudied in relation to climate distress. We examine the association between social (dis)connection and climate change anxiety among a sample of individuals, aged 16+, living in British Columbia, Canada. Cross-sectional online surveys administered between May and December 2021 were conducted with a sample of participants recruited via online social media advertisements. We conducted multivariable linear regression analyses to assess associations between social disconnection and climate change anxiety. Mediation analyses were also conducted to assess if generalized psychological distress mediated the pathways of interest. Findings revealed that (a) subjective social disconnection was associated with greater climate change anxiety, and (b) this effect was mediated by higher levels of generalized psychological distress. Dominance analyses revealed social disconnection and political orientation as key contributors to climate change anxiety. We conclude that building resilience through supportive social networks and communities may mitigate the harmful effects of climate change anxiety. Interventions may benefit from group-based and community-building modalities. Further research on such interventions is needed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".