Evaluation of water, sanitation and hygiene program outcomes shows knowledge-behavior gaps in Coast Province, Kenya
Bibliographic record
Abstract
INTRODUCTION: Water related diseases constitute a significant proportion of the burden of disease in Kenya. Water, sanitation and hygiene (WASH) programs are in operation nation-wide to address these challenges. This study evaluated the impact of the Sombeza Water and Sanitation Improvement Program (SWASIP) in Coast Province, Kenya. METHODS: This study is a cluster randomized, follow-up evaluation that compared baseline (2007) to follow-up (2013) indicators from 250 households. Twenty-five villages were selected with probability proportional to size sampling, and ten households were selected randomly from each village. Follow-up data were collected by in-person interviews using pre-tested questionnaires, and analyzed to compare indicators collected at baseline. Cross-sectional results from the follow-up data were also reported. RESULTS: Statistically significant improvements from baseline were observed in the proportions of respondents with latrine access at home, who washed their hands after defecation, who treated their household drinking water and the average time to collect water in the dry season. However, this study also observed significant decreases in the proportion of respondents who washed their hands before preparing their food, or feeding their children, and after attending to a child who has defecated. The analysis also revealed a knowledge-behavior gap in WASH behaviors. CONCLUSION: SWASIP contributed to improvements from baseline, but further progress still needs to be seen. The findings challenge the assumption that providing infrastructure and knowledge will result in behavior change. Further understanding of specific, non-knowledge predictors of WASH related behavior is needed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".