Moving from efficacy to effectiveness: using behavioural economics to improve the impact of WASH interventions
Bibliographic record
Abstract
Biological plausibility and randomized controlled efficacy trials justify the importance of clean and accessible water, improved sanitation, and hygiene in reducing morbidity and mortality rates. However, most health impacts can only take place if people use the improved services and practise hygienic behaviour. Despite considerable efforts in increasing access to water and sanitation services and promoting hygiene practices, few people chlorinate their water, wash their hands, and connect to sanitation systems, limiting the potential health impacts of development programmes. This paper explores cognitive and behavioural constraints identified in the behavioural economics literature and how interventions have successfully accounted for these constraints in their design to increase demand for services and positive habit formation. We then provide stylized examples of using behavioural economics solutions when framing information in hygiene campaigns, using new technologies to remind individuals to wash their hands at critical junctures, automating water purification processes, and designing ‘smart’ sanitation subsidies as practical opportunities for practitioners to incorporate these insights into project design to achieve greater impact.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".