The psychosocial impacts of slow onset climate change events among youth in LMICs: A rapid evidence review
Bibliographic record
Abstract
Introduction: Recent research has sought to understand the complex experiences of climate change distress, which are highly prevalent among youth and in low- and middle-income countries (LMICs). However, most studies have focused on acute climate disasters and clinically diagnosable psychological responses, leaving a gap in understanding about how gradual climate change events impact broader psychosocial health. Methods: This review examined how slow-onset events relate to psychosocial distress among youth in climate-vulnerable LMICs using the PRISMA Rapid Review methodology. Four databases were searched, namely Scopus, Web of Science, MEDLINE, and Cochrane. Results: The initial search yielded 853 results, of which 14 articles met the inclusion criteria for data extraction and analysis, which employed a framework synthesis approach. Studies identified direct impacts of slow onset events (e.g., changing precipitation patterns), as well as indirect impacts on communities (e.g., housing instability) and resources (e.g., loss of livestock). These disruptions are often compounded by pre-existing vulnerabilities such as public debt and inadequate basic services. Faced by these challenges, youth employed many coping strategies such as religion and meaning-focused coping, to help minimize psychosocial distress. Psychosocial responses varied from feelings of worry and numbness to severe outcomes like suicidal ideation. Conclusion: This review reveals the diversified experiences of climate change, which are closely tied to social and community contexts. A robust research agenda on the psychosocial effects of slow-onset environmental changes is vital to better understand and mitigate its long-term mental health impact on youth.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".