{"id":"W4313536866","doi":"10.2196/42401","title":"The Effect of the COVID-19 Pandemic on Digital Health–Seeking Behavior: Big Data Interrupted Time-Series Analysis of Google Trends","year":2023,"lang":"en","type":"article","venue":"Journal of Medical Internet Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Interrupted Time Series Analysis; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Time series; Series (stratigraphy); Interrupted time series; Big data; Computer science; Psychology; Medicine; Virology; Statistics; Data mining; Psychological intervention; Mathematics; Outbreak; Biology; Psychiatry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003093641,0.0004219108,0.0006269663,0.003010654,0.0003550221,0.0015397,0.0007359834,0.0008592657,0.001650529],"category_scores_gemma":[0.01577137,0.0002452909,0.001341532,0.005168376,0.0005933419,0.001188902,0.001004869,0.001157182,0.0006163357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007901328,"about_ca_system_score_gemma":0.0006296515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03090268,"about_ca_topic_score_gemma":0.01792474,"domain_scores_codex":[0.9976095,0.0008585224,0.0002947385,0.0004921918,0.0004431995,0.0003018947],"domain_scores_gemma":[0.9881265,0.006239516,0.003118671,0.001147176,0.0009336568,0.0004345178],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003376803,0.0001517671,0.9730667,0.0002503291,0.0004625585,0.0003186846,0.0007684395,0.008965139,0.0003536497,0.001444049,0.004265292,0.009615659],"study_design_scores_gemma":[0.00002471913,0.0001751112,0.9381525,0.00009686813,0.0002012028,0.0002055388,0.001312786,0.05273968,0.000439043,0.0009610555,0.005636935,0.00005459027],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9748814,0.00063532,0.002408716,0.000701781,0.00006421879,0.00007204548,0.01962521,0.0001073275,0.001503972],"genre_scores_gemma":[0.9799168,0.0001925527,0.001541353,0.00007599917,0.00004853609,0.00009824224,0.01769295,0.00002341322,0.0004102546],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03090268,"threshold_uncertainty_score":0.06144559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.189837808947712,"score_gpt":0.4971471565111147,"score_spread":0.3073093475634027,"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."}}