{"id":"W4353015353","doi":"10.2196/42206","title":"Predictors of Cyberchondria During the COVID-19 Pandemic: Cross-sectional Study Using Supervised Machine Learning","year":2023,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Psychosomatic Disorders and Their Treatments","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Fonds National de la Recherche Luxembourg","keywords":"Anxiety; Pandemic; Residence; Psychology; Distress; Coronavirus disease 2019 (COVID-19); Socioeconomic status; Inclusion (mineral); Clinical psychology; Cross-sectional study; Mental health; Medicine; Psychiatry; Demography; Environmental health; Social psychology; Disease; Sociology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.001962421,0.0001516964,0.0002676976,0.0004519946,0.0007865568,0.00005731294,0.0002182159,0.00007512506,0.0002773374],"category_scores_gemma":[0.000419546,0.00009419795,0.0001135433,0.00121637,0.0002872841,0.0001889831,0.0002133917,0.0006412065,0.00008267718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003297157,"about_ca_system_score_gemma":0.0002011118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004709994,"about_ca_topic_score_gemma":0.00003719457,"domain_scores_codex":[0.9971423,0.0005185176,0.0004391759,0.0002423064,0.001179075,0.0004786683],"domain_scores_gemma":[0.9985836,0.0005649587,0.00009868875,0.0003146825,0.0002304952,0.0002075962],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004405957,0.0005399202,0.9733576,0.0002423893,0.0003175979,0.00001975058,0.02366787,0.0001838254,0.001054207,0.00001613065,0.0000918798,0.00006825805],"study_design_scores_gemma":[0.004710014,0.0006731179,0.9805468,0.00006975887,0.00001312048,0.000051738,0.007921161,0.005265496,0.00005647127,0.0001755525,0.0004371914,0.00007952771],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970957,0.00005950483,0.00002474127,0.0001040507,0.0001311992,0.00145461,0.00004477394,0.000111237,0.0009741908],"genre_scores_gemma":[0.9981641,0.00005648376,0.00001260214,0.00001906249,0.0000628866,0.0001898158,0.00005614508,0.00002582807,0.001413122],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01574671,"threshold_uncertainty_score":0.6049638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.156326368550678,"score_gpt":0.4694355382632409,"score_spread":0.3131091697125629,"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."}}