{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001580065,0.0003852469,0.0003978684,0.0009504659,0.000537565,0.0007757391,0.0004290784,0.0006340529,0.001615342],"category_scores_gemma":[0.003566321,0.0003921377,0.0006646158,0.0009085984,0.0002775026,0.0005850683,0.0006801646,0.001339952,0.0004156895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004049634,"about_ca_system_score_gemma":0.0006410546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00674438,"about_ca_topic_score_gemma":0.007760408,"domain_scores_codex":[0.9992638,0.0002265061,0.00007860218,0.0001863203,0.0001010341,0.0001437009],"domain_scores_gemma":[0.9972935,0.0006008424,0.001024054,0.0002125853,0.0004030592,0.0004658919],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006670972,0.0002682777,0.9980128,0.000009659025,0.00004123242,0.00002443557,0.00006547661,0.0001023338,0.00005481141,0.000009543572,0.0001872568,0.001157521],"study_design_scores_gemma":[0.0000068343,0.0002070357,0.9975865,0.00001155462,0.00002095798,0.00007716333,0.0003019813,0.001595779,0.00003335688,0.0000173091,0.0001355756,0.00000604786],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985027,0.00006644569,0.0001994174,0.00005538546,0.000008653421,0.00005493789,0.0008840398,0.000006655918,0.0002217703],"genre_scores_gemma":[0.9970627,0.00009367965,0.0003852914,0.00004204062,0.00001425289,0.0001055957,0.002024202,0.000003954882,0.0002681663],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00674438,"threshold_uncertainty_score":0.01341027,"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."}}