Young adolescent recommendations for advancing water, food, and sanitation security in Kenya: Qualitative insights
Bibliographic record
Abstract
Climate change and related extreme weather events (EWE) exacerbate resource insecurities that threaten youth wellbeing. We conducted participatory mapping workshops (PMW) to generate recommendations for reducing food, water, and sanitation insecurity from young adolescents in Kenya. We conduced two-day multi-media PMWs that involved drawing, discussions, emoji descriptions, song creation, and writing, in six climate-affected Kenyan sites (Nairobi, Naivasha, Kilifi, Kisumu, Kalobeyei refugee settlement, Isiolo) with young adolescents aged 10–14 from 2022 to 2023. We applied framework thematic analysis informed by resource insecurity theory. Participants ( n = 118; mean age: 12.1, standard deviation: 1.33) included adolescent boys (50.8 %) and girls (49.2 %). Overarching themes included recommendations to advance food security (e.g., food affordability, food production, food safety, food access), water security (e.g., water quality and safety, water infrastructure, water sufficiency), and sanitation security (e.g., waste management, hygiene and health education, sanitation infrastructure). These recommendations spanned government (e.g., reduce food tax, provide seeds/fertilizer), community (e.g., mutual aid, hygiene awareness), and household (e.g., reduce water wastage, youth safety collecting resources) levels. Young adolescents identified multi-level solutions to advance water, food, and sanitation insecurity in climate-affected Kenyan regions. Climate and poverty interventions should amplify youth recommendations to advance health and wellbeing in Kenya. • Participatory mapping is an innovative method for young adolescent research. • Young adolescents in Kenya identified food, water, and sanitation daily stressors. • Young adolescents voiced clear solutions to food, water, and sanitation insecurity. • Young adolescents' solutions included infrastructure and behaviour change. • Findings support engaging youth in water, food, and sanitation decision-making.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".