{"id":"W2073558891","doi":"10.3362/1756-3488.2014.004","title":"Moving from efficacy to effectiveness: using behavioural economics to improve the impact of WASH interventions","year":2014,"lang":"en","type":"article","venue":"Waterlines","topic":"Child Nutrition and Water Access","field":"Nursing","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Impact","funders":"","keywords":"Sanitation; Psychological intervention; Hygiene; Nudge theory; Stylized fact; Business; Subsidy; Behavior change; Impact evaluation; Behavioral economics; Public economics; Marketing; Environmental health; Environmental economics; Psychology; Medicine; Economics; Engineering; Nursing; Environmental engineering; Social psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002539509,0.0001648794,0.0002727181,0.0001250374,0.0001206596,0.0001184685,0.0002776532,0.00005056637,0.00002628062],"category_scores_gemma":[0.00008662518,0.0001092373,0.0003370279,0.0001091105,0.00002981641,0.000147198,0.0001259218,0.00008884408,0.00002221075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001046479,"about_ca_system_score_gemma":0.000005913599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004969028,"about_ca_topic_score_gemma":0.0001239116,"domain_scores_codex":[0.9989672,0.0001463889,0.0003468468,0.0002537766,0.00006127993,0.0002244685],"domain_scores_gemma":[0.9992219,0.0001848473,0.00008808592,0.0003250098,0.00007802349,0.000102132],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.002515355,0.001254277,0.4216135,0.0002909826,0.0003086526,0.000002126775,0.007878296,0.03482843,0.4878779,0.00006523831,0.0005184195,0.0428469],"study_design_scores_gemma":[0.001578788,0.0005421297,0.7282814,0.0005346977,0.000106103,0.000003668554,0.0000719899,0.005211358,0.2625587,0.0007479162,0.00008756678,0.0002757385],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961665,0.0000168787,0.001667376,0.0006739534,0.0008051021,0.0005218547,0.00009182068,0.00004390177,0.00001258767],"genre_scores_gemma":[0.9987194,5.451254e-7,0.0007004451,0.0001191113,0.0003683174,0.00001640477,0.00003292832,0.00002936236,0.00001346353],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3066679,"threshold_uncertainty_score":0.7511716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03729875534082794,"score_gpt":0.3347479644845209,"score_spread":0.2974492091436929,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}