{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002142193,0.00005444285,0.0001397241,0.00002756935,0.00012856,0.00003700262,0.00004687455,0.00002299002,0.0005200293],"category_scores_gemma":[0.00004538564,0.00003937896,0.00007913859,0.00001536426,0.0002066682,0.0001429609,0.00003513431,0.00005333882,8.017859e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000897822,"about_ca_system_score_gemma":0.00008210664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003090047,"about_ca_topic_score_gemma":0.002038673,"domain_scores_codex":[0.999485,0.00004591175,0.0002204495,0.00009728238,0.0000587862,0.00009258821],"domain_scores_gemma":[0.9995355,0.0001316338,0.00009604765,0.0001278948,0.00006784659,0.00004109575],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006928084,0.001436149,0.004243044,0.01885139,0.0002218416,0.001357355,0.2924415,0.00004085033,0.001901992,0.576904,0.01087757,0.09103146],"study_design_scores_gemma":[0.006562594,0.003719651,0.003962394,0.002045619,0.0007240811,0.0009633735,0.3425266,0.0001872236,0.01869144,0.003444116,0.6164246,0.0007482455],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.988398,0.009767426,0.0001176888,0.0007333492,0.0001985759,0.0001788487,0.0001847644,0.000007115162,0.000414217],"genre_scores_gemma":[0.9968017,0.001220654,0.00009818547,0.0004319821,0.000160706,0.00003231009,0.00002424955,0.000006410795,0.001223803],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6055471,"threshold_uncertainty_score":0.5693957,"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."}}