Seek Potential in Engagement and Service Integration to Foster Community Resilience for Climate Anxiety: A Scoping Review
Bibliographic record
Abstract
Abstract Community resilience is increasingly considered a vital strategy to bounce back from climate-related mental health challenges. This study conducted a scoping review following Arksey and O’Malley’s five-step framework. Using MEDLINE, Embase, PsycINFO, and PubMed, the review focused on literature from 2020 to 2023 that discussed climate anxiety and community resilience. Inclusion criteria encompassed peer-reviewed studies that targeted climate anxiety as a health concern and proposed coping strategies promoting community resilience. Articles were assessed for their characteristics and engagement components in fostering adaptation to climate anxiety. The search yielded 159 citations, with 8 studies meeting the criteria after a full-text review. Findings revealed three primary strategies for coping with climate anxiety: climate activism, education, and relationship-building initiatives, with varying degrees of community engagement. Youth-centered approaches dominated, while marginalized groups, particularly women and Indigenous communities, were underrepresented. The literature emphasized engagement but lacked standardization in measuring resilience outcomes, suggesting a gap in evidence-based interventions and regulated frameworks. Climate anxiety represents a growing mental health concern that demands a proactive, community-based approach. Current coping strategies underscore the potential of resilience-building through social networks and engagement. Integrating climate anxiety interventions within existing health systems, such as the Integrated Youth Services (IYS), offers a promising pathway to address resource constraints and enhance accessibility. Future research should prioritize culturally sensitive methodologies, inclusion of underrepresented equity-deserving groups, and the development of preventive strategies to build resilient communities equipped with the capacity to cope with climate 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 | 0.019 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 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".