{"id":"W4288884738","doi":"10.1101/2022.07.29.22278191","title":"Has the pandemic enhanced and sustained digital health-seeking behaviour? A big data interrupted time-series analysis of Google Trends","year":2022,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Digital health; Pandemic; Proxy (statistics); Mainstream; Health care; Telehealth; Telemedicine; Political science; Business; Coronavirus disease 2019 (COVID-19); Medicine; Economic growth; Computer science; Economics","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":[0.005343142,0.0003146575,0.0005278423,0.00196642,0.0002854624,0.001828111,0.0007502532,0.0008797475,0.003141016],"category_scores_gemma":[0.02384114,0.0002001558,0.0009493211,0.004403724,0.0006659736,0.001140891,0.001038201,0.001110273,0.0007918163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008523749,"about_ca_system_score_gemma":0.0006274739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02667421,"about_ca_topic_score_gemma":0.01354796,"domain_scores_codex":[0.9963864,0.001723716,0.0003854087,0.0005680961,0.000547794,0.000388512],"domain_scores_gemma":[0.9814075,0.01128394,0.004190549,0.001555647,0.001172256,0.0003900555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005054684,0.0001484221,0.9572549,0.0004356687,0.0006098633,0.0003296586,0.001858287,0.008277803,0.0004112892,0.004342191,0.008446109,0.01738026],"study_design_scores_gemma":[0.00003520898,0.0002190496,0.9246533,0.0002066596,0.000266236,0.0002116225,0.003450797,0.0551909,0.0007188796,0.002594932,0.01238399,0.0000683394],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9629836,0.0008257869,0.005006896,0.002400771,0.000103289,0.0001027937,0.0252594,0.0001446693,0.003172773],"genre_scores_gemma":[0.9864253,0.0001647268,0.001547339,0.0001140697,0.00004947103,0.0000831966,0.01079886,0.00002775108,0.0007892537],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02667421,"threshold_uncertainty_score":0.05303788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07039955859805598,"score_gpt":0.3334362809787061,"score_spread":0.2630367223806502,"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."}}