Climate change and resource insecurity-related mental health stressors among young adolescents in Kenya: Qualitative multi-method insights
Bibliographic record
Abstract
While the links between extreme weather events and mental health have received growing attention, little is known about how climate change impacts adolescent mental health in low- and middle-income climate-affected settings. To address this gap, we conducted a multi-method qualitative study exploring how young adolescents (YAs) aged 10-14 years experience climate-related stressors across six regions in Kenya. Guided by the resource insecurity framework, we thematically analyzed Elder focus groups, YA walk-along interviews and YA participatory mapping workshops. Our findings revealed that food, water and sanitation insecurity contribute to psychological distress, including symptoms of depression and suicidality, and heighten concerns of community violence (e.g., assault, fighting). Water insecurity, particularly the time and distance required for collection, disrupted school attendance, while resource borrowing generated feelings of shame. Food insecurity and larger contexts of poverty were associated with substance use as a coping mechanism, which in turn contributed to school dropout, crime and gang involvement. Poverty also led some youth to run away from home. These findings highlight the urgent need for climate-informed mental health interventions that address co-occurring resource insecurities. To advance adolescent mental health and well-being in climate-affected settings, policy responses must be targeted and multilevel, engaging families, communities and institutions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".