{"id":"W4200178506","doi":"10.1016/j.heliyon.2021.e08535","title":"Rapid discovery of optimal messages for behavioral intervention: the case of Hungary and Covid-19","year":2021,"lang":"en","type":"article","venue":"Heliyon","topic":"Media Influence and Health","field":"Arts and Humanities","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"National Research, Development and Innovation Office; Nemzeti Kutatási, Fejlesztési és Innovaciós Alap; Innovációs és Technológiai Minisztérium; Magyar Tudományos Akadémia; Hungarian Scientific Research Fund; Szent István Egyetem; Nemzeti Kutatási Fejlesztési és Innovációs Hivatal; Magyar Agrár- és Élettudományi Egyetem","keywords":"Social distance; Mindset; Coronavirus disease 2019 (COVID-19); Pandemic; Psychology; Population; Distancing; Intervention (counseling); Social psychology; Compliance (psychology); Social isolation; Public relations; Applied psychology; Medicine; Political science; Computer science; Environmental health","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004940169,0.0001915125,0.0002591731,0.0008211265,0.002321012,0.001987471,0.0006015635,0.001265706,0.002900149],"category_scores_gemma":[0.01768739,0.0002527825,0.0002227468,0.0005772081,0.00144156,0.001384694,0.001735147,0.001143389,0.0002014159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002194954,"about_ca_system_score_gemma":0.00187238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004978248,"about_ca_topic_score_gemma":0.00607703,"domain_scores_codex":[0.9973264,0.001812514,0.00007564456,0.0001180273,0.0001737204,0.0004937521],"domain_scores_gemma":[0.9929795,0.005469046,0.0006911388,0.0002314771,0.0002105179,0.0004183443],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.003322132,0.005293605,0.3057674,0.0008903753,0.0001145578,0.0140053,0.3310103,0.002415329,0.01310272,0.01456545,0.0025883,0.3069245],"study_design_scores_gemma":[0.0004703348,0.003050539,0.3971475,0.0005386971,0.0001876329,0.001688805,0.5471333,0.0113956,0.009321136,0.01227622,0.0166513,0.0001389669],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964349,0.00004421526,0.0005942116,0.0008349792,0.00000693214,0.00007682482,0.00001426452,0.000007831947,0.001985837],"genre_scores_gemma":[0.9980275,0.00004783849,0.00143503,0.0001093384,0.000004481537,0.00003404206,0.00001280136,0.000002696018,0.0003261824],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004978248,"threshold_uncertainty_score":0.02612638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1029370100819429,"score_gpt":0.3595853004134098,"score_spread":0.2566482903314669,"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."}}