Climate change, resource insecurities and sexual and reproductive health among young adolescents in Kenya: a multi-method qualitative inquiry
Bibliographic record
Abstract
INTRODUCTION: Growing evidence supports linkages between climate change and extreme weather events (EWEs) and sexual and reproductive health (SRH) among adults. Yet knowledge gaps persist regarding climate-related experiences and pathways to SRH among young adolescents (YA). We conducted a multi-method qualitative study to explore climate change-related factors and linkages with SRH among YA aged 10-14 years in Kenya. METHODS: This six-site study was conducted in Nairobi's urban slum Mathare; Naivasha's flower farming community; Kisumu's fishing community; Isiolo's nomadic and pastoralist community; Kilifi's coastal smallholder farms and Kalobeyei refugee settlement. Methods involved: n=12 elder focus groups, n=60 YA walk-along interviews (WAIs) and n=12 2-day YA participatory mapping workshops (PMWs). We conducted codebook thematic analysis informed by the resource insecurity framework. RESULTS: Participants (n=297) included: elders (n=119; mean age: 60.6 years, SD: 7.9; men: 48.7%, women: 51.3%), YA WAI participants (n=60; mean age: 13.4, SD: 1.5; boys: 51.4%, girls: 48.6%) and YA PMW participants (n=118; mean age: 12.1, SD: 1.3; boys: 50.8%, girls: 49.2%). Narratives identified climate-related changes and EWEs increased existing resource insecurities that, in turn, were linked directly and indirectly with SRH vulnerabilities. Food and water insecurity contributed to YA missing school, sexual violence, transactional sex and exploitative relationships. Sanitation insecurity produced challenges regarding menstrual hygiene, sexual violence risks and transactional sex. Transactional sex and exploitative relationships were linked with unplanned pregnancy and sexually transmitted infection risks. Gender inequities increased girls' risks for violence and sexual exploitation, whereas boys were more prone to running away. CONCLUSION: We found that climate change exacerbated resource insecurities that may drive SRH outcomes among YA in Kenya. We developed a conceptual model to illustrate these pathways linking climate change, EWEs, resource insecurities and SRH. Climate-informed interventions should consider these pathways within larger social environmental contexts to advance young adolescent SRH in Kenya.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 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".